JP2018028688A - Decoding method and decoder - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a decoding method and a decoder that improve speech quality by frame loss processing.SOLUTION: A decoding method includes the steps of: synthesizing a high-frequency band signal according to a decoding result of a previous frame in a state where a current frame is determined to be a lost frame; determining a sub-frame gain of a plurality of sub-frames of the current frame according to a sub-frame gain of the sub-frames of at least one frame before the current frame and a gain slope between the sub-frames of the at least one frame; determining an overall gain of the current frame; and adjusting the synthesized high-frequency band signal according to the overall gain and the sub-frame gain of the plurality of sub-frames in order to obtain a high-frequency band signal of the current frame.SELECTED DRAWING: Figure 1

Description

  This application claims priority to Chinese Patent Application No. 201310298040.4 entitled “DECODING METHOD AND DECODING APPARATUS” filed with the Chinese Patent Office on July 16, 2013, which is hereby incorporated by reference in its entirety. Embedded in the book.

  The present invention relates to the field of encoding and decoding, and more specifically, to a decoding method and a decoding apparatus.

  With continuous technology advancement, user demands for sound quality are increasing. Increasing voice bandwidth is the main way to improve sound quality. In general, bandwidth extension techniques are used to increase bandwidth, and bandwidth extension techniques include time domain bandwidth extension techniques and frequency domain bandwidth extension techniques.

  In the time domain bandwidth expansion technology, the packet loss rate is a large factor affecting the signal quality. In the case of packet loss, it is necessary to repair lost frames as accurately as possible. The decoder side determines whether frame loss has occurred by analyzing the bitstream information. If no frame loss has occurred, normal decoding processing is performed. When frame loss has occurred, it is necessary to perform frame loss processing.

  When performing frame loss processing, the decoder side acquires the high frequency band signal according to the decoding result of the previous frame, and multiplies the overall gain of the previous frame by the set subframe gain and fixed attenuation coefficient. The gain adjustment is performed on the high frequency band signal using the overall gain acquired by the above process, and the final high frequency band signal is acquired.

  The subframe gain used during frame loss processing is a set value, so spectral discontinuity may occur, transitions before and after frame loss become discontinuous, noise phenomenon appears during signal reconstruction, Call quality will deteriorate.

  Embodiments of the present invention provide a decoding method and a decoding apparatus that can avoid or reduce a noise phenomenon during frame loss processing and thereby improve call quality.

  According to a first aspect, there is provided a decoding method, wherein in a situation where the current frame is determined to be a lost frame, the high frequency band signal according to the decoding result of the previous frame of the current frame And subframe gains of at least two subframes of the current frame according to a subframe gain of subframes of at least one frame prior to the current frame and a gain gradient between subframes of at least one frame. Determining the global gain of the current frame and subframes of at least two subframes to obtain the high frequency band signal of the current frame Adjusting the synthesized high frequency band signal according to the gain.

  In accordance with the first aspect, in a first possible embodiment, according to a subframe gain of a subframe of at least one frame prior to the current frame and a gain gradient between the subframes of at least one frame Determining the subframe gain of at least two subframes of the current frame includes starting a subframe gain of the current frame according to a subframe gain of the subframe of at least one frame and a gain gradient between the subframes of at least one frame Determining the subframe gain of the current frame, and subtracting the start subframe of at least two subframes according to the subframe gain of the start subframe of the current frame and the gain gradient between the subframes of at least one frame Subfre And determining the subframe gain beam.

  In accordance with the first possible embodiment, in a second possible embodiment, the current frame according to a subframe gain of a subframe of at least one frame and a gain gradient between the subframes of at least one frame Determining the sub-frame gain of the starting sub-frame of the current frame according to the gain gradient between the sub-frames of the previous frame of the current frame and the last sub-frame of the previous frame of the current frame and the starting sub-frame of the current frame Estimating a first gain gradient between, and estimating a subframe gain of a last subframe of a previous frame of the current frame and a subframe gain of a starting subframe of the current frame according to a first gain gradient Including.

  In a third possible embodiment, compliant with the second possible embodiment, the last subframe of the previous frame of the current frame according to the gain gradient between the subframes of the previous frame of the current frame and The step of estimating a first gain gradient between a starting subframe of the current frame includes a weighted average for a gain gradient between at least two subframes of a previous frame of the current frame, and the first The step of obtaining a gain gradient includes the step of performing weighted averaging, wherein the gain gradient between subframes of the previous frame of the current frame close to the current frame occupies a greater weight.

を使用して取得され、GainGradFEC[0]は、第1のゲイン勾配であり、GainGrad[n-1,j]は、現在のフレームの前回のフレームの第jのサブフレームと第(j+1)のサブフレームとの間のゲイン勾配であり、α_j+1≧α_jであり、

であり、j=0、1、2、・・・、I-2であり、開始サブフレームのサブフレームゲインは、
GainShapeTemp[n,0]=GainShape[n-1,I-1]+φ₁*GainGradFEC[0]、
GainShape[n,0]=GainShapeTemp[n,0]*φ₂を使用して取得され、
GainShape[n-1,I-1]は、第(n-1)のフレームの第(I-1)のサブフレームのサブフレームゲインであり、GainShape[n,0]は、現在のフレームの開始サブフレームのサブフレームゲインであり、GainShapeTemp[n,0]は、開始サブフレームのサブフレームゲインの中間値であり、0≦φ₁≦1.0であり、0<φ₂≦1.0であり、φ₁は、現在のフレームの前に受信した最終フレームのフレームクラスおよび第1のゲイン勾配の正または負の符号を使用して決定され、φ₂は、現在のフレームの前に受信した最終フレームのフレームクラスおよび現在のフレームより前の連続したロストフレームの数を使用して決定される。 Referring to the second possible embodiment or the third possible embodiment, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, If each frame contains I subframes, the first gain slope is

GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, j] is the jth subframe and (j + 1) th of the previous frame of the current frame ) Gain gradient with respect to the subframe, α _{j + 1} ≧ α _j ,

J = 0, 1, 2,..., I-2, and the subframe gain of the start subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + φ ₁ * GainGradFEC [0],
GainShape [n, 0] = GainShapeTemp [n, 0] * φ 2 to be obtained using,
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the (n-1) th frame, and GainShape [n, 0] is the start of the current frame The subframe gain of the subframe, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 ≦ φ ₁ ≦ 1.0, 0 <φ ₂ ≦ 1.0, φ ₁ Is determined using the frame class of the last frame received before the current frame and the positive or negative sign of the first gain gradient, and φ ₂ is the frame of the last frame received before the current frame Determined using class and number of consecutive lost frames prior to current frame.

  In a fifth possible embodiment, compliant with the second possible embodiment, the last subframe of the previous frame of the current frame according to the gain gradient between the subframes of the previous frame of the current frame and The step of estimating the first gain gradient between the start subframe of the current frame is the first gain gradient of the subframe before the last subframe of the previous frame of the current frame and the current frame. Using a gain gradient between the last subframe of the previous frame.

In a sixth possible embodiment, compliant with the second or fifth possible embodiment, the previous frame of the current frame is the (n-1) th frame, and the current frame is the first If there are n frames and each frame contains I subframes, the first gain gradient is obtained using GainGradFEC [0] = GainGrad [n-1, I-2] and GainGradFEC [0] is the first gain gradient, and GainGrad [n−1, I−2] is the (I−1) th subframe of the (I−2) th subframe and the previous frame of the current frame. It is the gain gradient between subframes, and the subframe gain of the start subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + λ ₁ * GainGradFEC [0],
GainShapeTemp [n, 0] = min (λ ₂ * GainShape [n-1, I-1], GainShapeTemp [n, 0]),
Obtained using GainShape [n, 0] = max (λ ₃ * GainShape [n-1, I-1], GainShapeTemp [n, 0])
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the previous frame of the current frame, and GainShape [n, 0] is the subframe of the start subframe Gain, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 <λ ₁ <1.0, 1 <λ ₂ <2, 0 <λ ₃ <1.0 Yes, λ ₁ is determined using multiple relationships between the frame class of the last frame received before the current frame and the subframe gains of the last two subframes of the previous frame of the current frame, λ ₂ and λ ₃ are determined using the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame.

  In a seventh possible embodiment, compliant with any one of the second to sixth possible embodiments, the subframe gain of the last subframe of the previous frame of the current frame and the first Estimating the subframe gain of the starting subframe of the current frame according to a gain gradient of 1 includes subframe gain and first gain gradient of the last subframe of the previous frame of the current frame, and before the current frame. Estimating the subframe gain of the starting subframe of the current frame according to the frame class of the last frame received and the number of consecutive lost frames prior to the current frame.

  In an eighth possible embodiment, compliant with any one of the first to seventh possible embodiments, the subframe gain of the starting subframe of the current frame and at least one of the frames Determining the subframe gain of the other subframes excluding the starting subframe of at least two subframes according to the gain gradient between the subframes is based on the gain gradient between the subframes of at least one frame. Estimating a gain gradient between at least two subframes of and at least two subframes according to a gain gradient between at least two subframes of the current frame and a subframe gain of a starting subframe of the current frame Subframes of other subframes excluding the start subframe Estimating a lemma gain.

  In accordance with the eighth possible embodiment, in a ninth possible embodiment, each frame includes I subframes, and the current frame according to a gain gradient between the subframes of at least one frame Estimating a gain gradient between at least two subframes of the current frame includes a gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame of the current frame and the current frame Performing a weighted averaging on the gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame of the previous frame, and the i-th subframe and the i-th subframe of the current frame a step of estimating a gain gradient with respect to a subframe of (i + 1), i = 0, 1,..., I-2, and the i-th sub of the previous frame of the current frame Frame and (i + 1) th support The weight occupied by the gain gradient between frames is the weight occupied by the gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame of the previous frame of the current frame Larger, including steps.

In a tenth possible embodiment, compliant with the eighth or ninth possible embodiment, the previous frame of the current frame is the (n-1) th frame, and the current frame is the first If there are n frames, the gain gradient between at least two subframes of the current frame is
GainGradFEC [i + 1] = GainGrad [n-2, i] * β ₁ + GainGrad [n-1, i] * β ₂
GainGradFEC [i + 1] is the gain gradient between the i-th subframe and the (i + 1) -th subframe, and GainGrad [n-2, i] is the previous frame of the current frame. Gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, and GainGrad [n−1, i] is the i-th subframe of the previous frame of the current frame. Gain gradient between the frame and the (i + 1) th subframe, β ₂ > β ₁ , β ₂ + β ₁ = 1.0, i = 0, 1, 2,. I-2, and the subframe gain of other subframes excluding the start subframe of at least two subframes is
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] * β ₃ ,
Determined using GainShape [n, i] = GainShapeTemp [n, i] * β ₄
GainShape [n, i] is the subframe gain of the i-th subframe of the current frame, and GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame 0 ≦ β ₃ ≦ 1.0, 0 <β ₄ ≦ 1.0, and β ₃ is a plurality of relationships between GainGrad [n−1, i] and GainGrad [n−1, i + 1] and Determined using the positive or negative sign of GainGrad [n-1, i + 1], where β ₄ is the frame class of the last frame received before the current frame and consecutive lost before the current frame Determined using the number of frames.

  In an eleventh possible embodiment, compliant with the eighth possible embodiment, each frame includes I subframes, and the current frame according to a gain gradient between the subframes of at least one frame The step of estimating the gain gradient between at least two subframes is to perform a weighted averaging on I gain gradients between (I + 1) subframes prior to the i-th subframe of the current frame. Performing a step of estimating a gain gradient between the i-th subframe and the (i + 1) -th subframe of the current frame, i = 0, 1, ..., I-2 And the gain gradient between subframes close to the i-th subframe occupies a greater weight.

In a twelfth possible embodiment, compliant with the eighth or eleventh possible embodiment, the previous frame of the current frame is the (n-1) th frame and the current frame is the first If there are n frames and each frame contains 4 subframes, the gain gradient between at least two subframes of the current frame is
GainGradFEC [1] = GainGrad [n-1,0] * γ ₁ + GainGrad [n-1,1] * γ ₂
+ GainGrad [n-1,2] * γ ₃ + GainGradFEC [0] * γ ₄ ,
GainGradFEC [2] = GainGrad [n-1,1] * γ ₁ + GainGrad [n-1,2] * γ ₂
+ GainGradFEC [0] * γ ₃ + GainGradFEC [1] * γ ₄ ,
GainGradFEC [3] = GainGrad [n-1,2] * γ ₁ + GainGradFEC [0] * γ ₂
Determined using + GainGradFEC [1] * γ ₃ + GainGradFEC [2] * γ ₄
GainGradFEC [j] is the gain gradient between the jth and (j + 1) th subframe of the current frame, and GainGrad [n-1, j] is the previous gradient of the current frame. Is the gain gradient between the jth subframe and the (j + 1) th subframe of the frame, j = 0, 1, 2,..., I-2, and γ ₁ + γ ₂ + γ ₃ + γ ₄ = 1.0, γ ₄ > γ ₃ > γ ₂ > γ ₁ and γ ₁ , γ ₂ , γ ₃ , and γ ₄ are determined using the frame class of the last frame received Subframe gains of other subframes excluding the start subframe of at least two subframes,
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i], where i = 1, 2, 3 and GainShapeTemp [n, 0] is the first gain gradient,
GainShapeTemp [n, i] = min (γ ₅ * GainShape [n-1, i], GainShapeTemp [n, i]),
GainShape [n, i] = max (γ ₆ * GainShape [n-1, i], GainShapeTemp [n, i])
i = 1, 2, and 3, GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame, and GainShape [n, i] is the subframe gain of i subframes, γ ₅ and γ ₆ are determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame, where 1 <γ ₅ < 2 and 0 ≦ γ ₆ ≦ 1.

  In a thirteenth possible embodiment, compliant with any one of the eighth to twelfth possible embodiments, the gain gradient between at least two subframes of the current frame and the current frame Estimating the subframe gain of other subframes excluding the starting subframe of at least two subframes according to the subframe gain of the starting subframe of the current subframe, the gain gradient between at least two subframes of the current frame And the start of at least two subframes according to the subframe gain of the starting subframe of the current frame and the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame Subframe gay of other subframes excluding subframes Estimating the process.

  In a fourteenth possible embodiment, in accordance with the first aspect or any one of the previous possible embodiments, the step of estimating the overall gain of the current frame comprises: Estimating the overall gain gradient of the current frame according to the frame class of the last frame received before the frame and the number of consecutive lost frames before the current frame; and Estimating the overall gain of the current frame according to the overall gain of the previous frame.

  In accordance with the fourteenth possible embodiment, in a fifteenth possible embodiment, the overall gain of the current frame is determined using GainFrame = GainFrame_prevfrm * GainAtten, where GainFrame is The overall gain of the frame, GainFrame_prevfrm is the overall gain of the previous frame of the current frame, 0 <GainAtten ≦ 1.0, GainAtten is the overall gain gradient, GainAtten is the receive And the number of consecutive lost frames prior to the current frame.

  According to a second aspect, there is provided a decoding method, wherein in a situation where the current frame is determined to be a lost frame, the high frequency band signal according to the decoding result of the previous frame of the current frame A step of determining a subframe gain of at least two subframes of the current frame, a frame class of the last frame received before the current frame, and successive lost frames before the current frame. Estimating the overall gain gradient of the current frame according to a number, estimating the overall gain of the current frame according to the overall gain gradient and the overall gain of the previous frame of the current frame; Overall gain and less to get the high frequency band signal of the current frame Also in accordance with the sub-frame gains of two subframes, and adjusting the synthesized high frequency band signals.

  In accordance with the second aspect, in a first possible embodiment, the overall gain of the current frame is determined using GainFrame = GainFrame_prevfrm * GainAtten, where GainFrame is the entire current frame. GainFrame_prevfrm is the overall gain of the previous frame of the current frame, 0 <GainAtten ≦ 1.0, GainAtten is the overall gain slope, GainAtten is the last frame received Frame class and the number of consecutive lost frames prior to the current frame.

  According to a third aspect, there is provided a decoding device, wherein in a situation where the current frame is determined to be a lost frame, the high frequency band signal according to the decoding result of the previous frame of the current frame And at least two of the current frame according to a generation module and a subframe gain of a subframe of at least one frame prior to the current frame and a gain gradient between the subframes of at least one frame A determination module configured to determine a subframe gain of the subframe and to determine an overall gain of the current frame, and to obtain an overall gain and Subframe gain of at least two subframes determined by the determination module Thus, configured to adjust the high frequency band signal synthesized by the generation module, and an adjusting module.

  In a first possible embodiment, in accordance with the third aspect, the determination module is configured to determine the current according to a subframe gain of a subframe of at least one frame and a gain gradient between the subframes of at least one frame. Determine the subframe gain of the start subframe of the frame and remove the start subframe of at least two subframes according to the subframe gain of the start subframe of the current frame and the gain gradient between the subframes of at least one frame The subframe gain of another subframe is determined.

  In a second possible embodiment, which is compliant with the first possible embodiment of the third aspect, the determination module is configured to determine the current frame according to a gain gradient between subframes of the previous frame of the current frame. Estimate the first gain gradient between the last subframe of the previous frame and the start subframe of the current frame, and the subframe gain and first gain gradient of the last subframe of the previous frame of the current frame To estimate the subframe gain of the starting subframe of the current frame.

  In a third possible embodiment, which is compliant with the second possible embodiment of the third aspect, the determination module is for a gain gradient between at least two subframes of the previous frame of the current frame. When obtaining the first gain gradient and performing the weighted average, the gain gradient between subframes of the previous frame of the current frame close to the current frame occupies a greater weight .

を使用して取得され、GainGradFEC[0]は、第1のゲイン勾配であり、GainGrad[n-1,j]は、現在のフレームの前回のフレームの第jのサブフレームと第(j+1)のサブフレームとの間のゲイン勾配であり、α_j+1≧α_jであり、

であり、j=0、1、2、・・・、I-2であり、開始サブフレームのサブフレームゲインは、
GainShapeTemp[n,0]=GainShape[n-1,I-1]+φ₁*GainGradFEC[0]、
GainShape[n,0]=GainShapeTemp[n,0]*φ₂を使用して取得され、
GainShape[n-1,I-1]は、第(n-1)のフレームの第(I-1)のサブフレームのサブフレームゲインであり、GainShape[n,0]は、現在のフレームの開始サブフレームのサブフレームゲインであり、GainShapeTemp[n,0]は、開始サブフレームのサブフレームゲインの中間値であり、0≦φ₁≦1.0であり、0<φ₂≦1.0であり、φ₁は、現在のフレームの前に受信した最終フレームのフレームクラスおよび第1のゲイン勾配の正または負の符号を使用して決定され、φ₂は、現在のフレームの前に受信した最終フレームのフレームクラスおよび現在のフレームより前の連続したロストフレームの数を使用して決定される。 In a fourth possible embodiment, compliant with the first possible embodiment of the third aspect or the second possible embodiment of the third aspect, the previous frame of the current frame is the first If (n-1) frames, the current frame is the nth frame, and each frame includes I subframes, then the first gain gradient is

GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, j] is the jth subframe and (j + 1) th of the previous frame of the current frame ) Gain gradient with respect to the subframe, α _{j + 1} ≧ α _j ,

J = 0, 1, 2,..., I-2, and the subframe gain of the start subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + φ ₁ * GainGradFEC [0],
GainShape [n, 0] = GainShapeTemp [n, 0] * φ 2 to be obtained using,
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the (n-1) th frame, and GainShape [n, 0] is the start of the current frame The subframe gain of the subframe, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 ≦ φ ₁ ≦ 1.0, 0 <φ ₂ ≦ 1.0, φ ₁ Is determined using the frame class of the last frame received before the current frame and the positive or negative sign of the first gain gradient, and φ ₂ is the frame of the last frame received before the current frame Determined using class and number of consecutive lost frames prior to current frame.

  In a fifth possible embodiment, which is compliant with the second possible embodiment of the third aspect, the determination module, as the first gain gradient, is the last subframe of the previous frame of the current frame. Use the gain gradient between the previous subframe and the last subframe of the previous frame of the current frame.

In a sixth possible embodiment, in accordance with the second or fifth possible embodiment of the third aspect, the previous frame of the current frame is the (n-1) th frame; If the current frame is the nth frame and each frame contains I subframes, the first gain gradient uses GainGradFEC [0] = GainGrad [n-1, I-2] GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, I-2] is the (I-2) th subframe and the previous frame of the current frame (( I-1) is the gain gradient between the subframe and the subframe gain of the start subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + λ ₁ * GainGradFEC [0],
GainShapeTemp [n, 0] = min (λ ₂ * GainShape [n-1, I-1], GainShapeTemp [n, 0]),
Obtained using GainShape [n, 0] = max (λ ₃ * GainShape [n-1, I-1], GainShapeTemp [n, 0])
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the previous frame of the current frame, and GainShape [n, 0] is the subframe of the start subframe Gain, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 <λ ₁ <1.0, 1 <λ ₂ <2, 0 <λ ₃ <1.0 Yes, λ ₁ is determined using multiple relationships between the frame class of the last frame received before the current frame and the subframe gains of the last two subframes of the previous frame of the current frame, λ ₂ and λ ₃ are determined using the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame.

  In a seventh possible embodiment, which is compliant with any one of the second to sixth possible embodiments of the third aspect, the decision module is configured for the previous frame of the current frame. The start subframe of the current frame according to the subframe gain and first gain gradient of the last subframe, and the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. Estimate the subframe gain.

  In an eighth possible embodiment, in accordance with any one of the first to seventh possible embodiments of the third aspect, the determination module is between subframes of at least one frame. Estimate the gain gradient between at least two subframes of the current frame according to the gain gradient of and at least two according to the gain gradient between at least two subframes of the current frame and the subframe gain of the starting subframe of the current frame The subframe gains of other subframes excluding the start subframe of the subframes are estimated.

  In a ninth possible embodiment, which is compliant with the eighth possible embodiment of the third aspect, each frame comprises I subframes, and the decision module The gain gradient between the i-th subframe and the (i + 1) -th subframe of the frame and the i-th subframe and (i + 1) -th subframe of the previous frame of the previous frame of the current frame Perform weighted averaging on the gain gradient between frames, estimate the gain gradient between the i-th subframe and the (i + 1) -th subframe of the current frame, i = 0, 1, ..., I-2, and the weight occupied by the gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame of the current frame is the previous frame of the current frame The i-th subframe and (i + 1) -th subframe of the previous frame Weights greater than occupied by the gain gradients between the beam.

In a tenth possible embodiment, compliant with the eighth or ninth possible embodiment of the third aspect, the gain gradient between at least two subframes of the current frame is
GainGradFEC [i + 1] = GainGrad [n-2, i] * β ₁ + GainGrad [n-1, i] * β ₂
GainGradFEC [i + 1] is the gain gradient between the i-th subframe and the (i + 1) -th subframe, and GainGrad [n-2, i] is the previous frame of the current frame. Gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, and GainGrad [n−1, i] is the i-th subframe of the previous frame of the current frame. Gain gradient between the frame and the (i + 1) th subframe, β ₂ > β ₁ , β ₂ + β ₁ = 1.0, i = 0, 1, 2,. I-2, and the subframe gain of other subframes excluding the start subframe of at least two subframes is
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] * β ₃ ,
Determined using GainShape [n, i] = GainShapeTemp [n, i] * β ₄
GainShape [n, i] is the subframe gain of the i-th subframe of the current frame, and GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame 0 ≦ β ₃ ≦ 1.0, 0 <β ₄ ≦ 1.0, and β ₃ is a plurality of relationships between GainGrad [n−1, i] and GainGrad [n−1, i + 1] and Determined using the positive or negative sign of GainGrad [n-1, i + 1], where β ₄ is the frame class of the last frame received before the current frame and consecutive lost before the current frame Determined using the number of frames.

  In an eleventh possible embodiment, in accordance with the eighth possible embodiment of the third aspect, the decision module comprises (I + 1) prior to the i th subframe of the current frame. Perform weighted averaging on I gain gradients between subframes of the current frame, estimate the gain gradient between the i-th subframe and the (i + 1) th subframe of the current frame, and i = 0 , 1,..., I-2, and the gain gradient between subframes close to the i-th subframe occupies a greater weight.

In a twelfth possible embodiment, in accordance with the eighth or eleventh possible embodiment of the third aspect, the previous frame of the current frame is the (n-1) th frame; If the current frame is the nth frame and each frame contains 4 subframes, then the gain gradient between at least two subframes of the current frame is
GainGradFEC [1] = GainGrad [n-1,0] * γ ₁ + GainGrad [n-1,1] * γ ₂
+ GainGrad [n-1,2] * γ ₃ + GainGradFEC [0] * γ ₄ ,
GainGradFEC [2] = GainGrad [n-1,1] * γ ₁ + GainGrad [n-1,2] * γ ₂
+ GainGradFEC [0] * γ ₃ + GainGradFEC [1] * γ ₄ ,
GainGradFEC [3] = GainGrad [n-1,2] * γ ₁ + GainGradFEC [0] * γ ₂
Determined using + GainGradFEC [1] * γ ₃ + GainGradFEC [2] * γ ₄
GainGradFEC [j] is the gain gradient between the jth and (j + 1) th subframe of the current frame, and GainGrad [n-1, j] is the previous gradient of the current frame. Is the gain gradient between the jth subframe and the (j + 1) th subframe of the frame, j = 0, 1, 2,..., I-2, and γ ₁ + γ ₂ + γ ₃ + γ ₄ = 1.0, γ ₄ > γ ₃ > γ ₂ > γ ₁ and γ ₁ , γ ₂ , γ ₃ , and γ ₄ are determined using the frame class of the last frame received Subframe gains of other subframes excluding the start subframe of at least two subframes,
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i], where i = 1, 2, 3 and GainShapeTemp [n, 0] is the first gain gradient,
GainShapeTemp [n, i] = min (γ ₅ * GainShape [n-1, i], GainShapeTemp [n, i]),
GainShape [n, i] = max (γ ₆ * GainShape [n-1, i], GainShapeTemp [n, i])
GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame, i = 1, 2, and 3, and GainShape [n, i] is the 1st subframe gain of the current frame. subframe gain of i subframes, γ ₅ and γ ₆ are determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame, where 1 <γ ₅ < 2 and 0 ≦ γ ₆ ≦ 1.

  In a thirteenth possible embodiment, according to any one of the eighth to twelfth possible embodiments, the determination module is configured to obtain a gain gradient between at least two subframes of the current frame. And the start of at least two subframes according to the subframe gain of the starting subframe of the current frame and the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame The subframe gain of other subframes excluding the subframe is estimated.

  In a fourteenth possible embodiment, in accordance with the third aspect or any one of the previous possible embodiments, the decision module is configured to receive a frame of the last frame received prior to the current frame. Estimate the overall gain gradient of the current frame according to the class and the number of consecutive lost frames before the current frame, and the overall gain gradient and the overall gain of the previous frame of the current frame of the current frame To estimate the overall gain of the current frame.

  In a fifteenth possible embodiment, in accordance with the fourteenth possible embodiment of the third aspect, the overall gain of the current frame is determined using GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the overall gain of the current frame, GainFrame_prevfrm is the overall gain of the previous frame of the current frame, 0 <GainAtten ≦ 1.0, and GainAtten is the overall gain gradient , GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame.

  According to a fourth aspect, there is provided a decoding device, wherein in a situation where the current frame is determined to be a lost frame, the high frequency band signal according to the decoding result of the previous frame of the current frame A generation module configured to synthesize a subframe gain of at least two subframes of the current frame and a frame class of the last frame received before the current frame and prior to the current frame Estimate the overall gain gradient of the current frame according to the number of consecutive lost frames, and estimate the overall gain of the current frame according to the overall gain gradient and the overall gain of the previous frame of the current frame Configured in order to obtain a high frequency band signal of the current frame Specific in accordance with the sub-frame gains of at least two sub-frames is determined by the gain and determination module, configured to adjust the high frequency band signal synthesized by the generation module, and an adjusting module.

  In a first possible embodiment, compliant with the fourth aspect, GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the overall gain of the current frame, and GainFrame_prevfrm is the previous time of the current frame. Is the overall gain of the frame, 0 <GainAtten ≦ 1.0, GainAtten is the overall gain slope, GainAtten is the frame class of the last frame received and the consecutive lost frames before the current frame Is determined using the number of

  In the embodiment of the present invention, when it is determined that the current frame is a lost frame, the subframe gain of the subframe before the current frame and the gain gradient between the subframes before the current frame are determined. A subframe gain of a subframe of the current frame is determined, and the high frequency band signal is adjusted using the determined subframe gain of the current frame. The subframe gain of the current frame is obtained according to the slope (which is the trend of change) between the subframe gains of the subframes before the current frame, so that the transition before and after the frame loss is more continuous This reduces noise during signal reconstruction and improves call quality.

  To describe the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings required for describing the embodiments of the present invention. The accompanying drawings in the following description show only some embodiments of the present invention, and it is obvious that those skilled in the art can further derive other drawings from these accompanying drawings without creative efforts. I will.

4 is a schematic flowchart of a decoding method according to an embodiment of the present invention; 6 is a schematic flowchart of a decoding method according to another embodiment of the present invention; FIG. 6 is a schematic illustration of a trend of a previous frame change in a subframe gain of a current frame according to an embodiment of the present invention; FIG. 6 is a schematic diagram of a previous frame change trend of a subframe gain of a current frame according to another embodiment of the present invention; FIG. 6 is a schematic illustration of a previous frame change trend of a subframe gain of a current frame according to yet another embodiment of the present invention. FIG. FIG. 4 is a schematic diagram of a process for estimating a first gain gradient according to an embodiment of the present invention. FIG. 4 is a schematic diagram of a process for estimating a gain gradient between at least two subframes of a current frame according to an embodiment of the present invention. 4 is a schematic flowchart of a decoding process according to an embodiment of the present invention. FIG. 4 is a schematic structural diagram of a decoding device according to an embodiment of the present invention; FIG. 6 is a schematic structural diagram of a decoding device according to another embodiment of the present invention; FIG. 6 is a schematic structural diagram of a decoding device according to another embodiment of the present invention; FIG. 4 is a schematic structural diagram of a decoding device according to an embodiment of the present invention;

  The following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are a part rather than all of the embodiments of the present invention. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

  In order to reduce the computational complexity and processing delay for the codec during audio signal processing, frame division processing is typically performed on the audio signal, i.e., the audio signal is divided into a plurality of frames. Furthermore, when conversation occurs, glottal vibrations have a specific frequency (corresponding to the pitch period). In situations with a relatively short pitch period, if the frame is too long, multiple pitch periods may exist within a single frame, resulting in an incorrect calculation of the pitch period. May be divided into a plurality of subframes.

  In time domain bandwidth extension technology, during encoding, first, the core encoder encodes the low frequency band information of the signal to obtain parameters such as pitch period, algebraic codebook, and respective gains. Second, a core encoder that performs linear predictive coding (LPC) analysis on the high frequency band information of the signal to obtain high frequency band LPC parameters, thereby obtaining an LPC synthesis filter Obtains high frequency band excitation signal by calculation based on parameters such as pitch period, algebraic codebook, and respective gain, and synthesizes high frequency band signal from high frequency band excitation signal using LPC synthesis filter Then, the core encoder compares the original high frequency band signal with the synthesized high frequency band signal to obtain subframe gain and overall gain Finally, the core encoder converts the LPC parameters to (Linear Spectrum Frequency, LSF) parameters, and quantizes and encodes the LSF parameters, subframe gain, and overall gain.

  During decoding, first, dequantization is performed on the LSF parameters, subframe gain, and overall gain, and the LSF parameters are converted to LPC parameters, thereby obtaining an LPC synthesis filter. In addition, the core decoder is used to obtain parameters such as pitch period, algebraic codebook, and respective gain, and the high frequency band excitation signal is converted into parameters such as pitch period, algebraic codebook, and respective gain. And using LPC synthesis filters to synthesize the high frequency band signal from the high frequency band excitation signal, and finally perform gain adjustment on the high frequency band signal according to the subframe gain and overall gain, Recover the high frequency band signal of the frame.

  According to this embodiment of the present invention, it can be determined whether or not a frame loss has occurred in the current frame by analyzing the bitstream information. If no frame loss has occurred in the current frame, the normal decoding process described above is performed. When a frame loss occurs in the current frame, that is, when the current frame is a lost frame, it is necessary to perform a frame loss process, that is, to recover the lost frame.

  FIG. 1 is a schematic flowchart of a decoding method according to an embodiment of the present invention. The method in FIG. 1 may be performed by a decoder and includes:

  110: If the current frame is determined to be a lost frame, a high frequency band signal is synthesized according to the decoding result of the previous frame of the current frame.

  For example, the decoder side determines whether frame loss has occurred by analyzing the bitstream information. If no frame loss has occurred, normal decoding processing is performed. If a frame loss has occurred, a frame loss process is performed. During the frame loss process, firstly, the high frequency band excitation signal is generated according to the decoding parameter of the previous frame, and secondly, the LPC parameter of the previous frame is duplicated and used as the LPC parameter of the current frame, Thereby, an LPC synthesis filter is obtained, and finally, a synthesized high frequency band signal is obtained from the high frequency band excitation signal using the LPC synthesis filter.

  120: Determine a subframe gain of at least two subframes of the current frame according to a subframe gain of a subframe of at least one frame prior to the current frame and a gain gradient between the subframes of at least one frame.

  The subframe gain of a subframe may refer to the ratio of the difference between the combined high frequency band signal of the subframe and the original high frequency band signal relative to the combined high frequency band signal. For example, the subframe gain may refer to the ratio of the difference between the amplitude of the combined high frequency band signal of the subframe and the amplitude of the original high frequency band signal relative to the amplitude of the combined high frequency band signal.

  The gain gradient between subframes is used to indicate the trend and degree of change, i.e., the gain change in subframe gain between adjacent subframes. For example, the gain gradient between the first subframe and the second subframe may refer to the difference between the subframe gain of the second subframe and the subframe gain of the first subframe. Good. This embodiment of the present invention is not limited thereto. For example, the gain gradient between subframes also refers to the subframe gain attenuation factor.

  For example, the gain change from the last subframe of the previous frame to the start subframe (which is the first subframe) of the current frame is determined according to the trend and degree of subframe gain change between the subframes of the previous frame. The subframe gain of the start subframe of the current frame may be estimated using the gain change and the subframe gain of the last subframe of the previous frame, and then the subframe gain of the current frame Is estimated according to the trend and degree of subframe gain change between subframes of at least one frame before the current frame, and finally the subframe gain of another subframe of the current frame , Gain change and estimated subframe gain for start subframe May be used to estimate.

  130: Determines the overall gain of the current frame.

  The overall gain of the frame may refer to the ratio of the difference between the combined high frequency band signal of the frame and the original high frequency band signal relative to the combined high frequency band signal. For example, the overall gain may indicate the ratio of the difference between the amplitude of the combined high frequency band signal and the amplitude of the original high frequency band signal relative to the amplitude of the combined high frequency band signal.

  The overall gain gradient is used to indicate the trend and degree of overall gain change between adjacent frames. The overall gain slope between a frame and another frame may refer to the difference between the overall gain of the frame and the overall gain of another frame. This embodiment of the present invention is not limited thereto. For example, the overall gain gradient between one frame and another frame may also refer to the overall gain attenuation factor.

  For example, the overall gain of the current frame may be estimated by multiplying the overall gain of the previous frame of the current frame by a fixed attenuation factor. Specifically, in this embodiment of the invention, the overall gain gradient is determined according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. Alternatively, the overall gain of the current frame may be estimated according to the determined overall gain gradient.

  140: Adjust (or control) the synthesized high frequency band signal according to the overall gain and the subframe gain of at least two subframes to obtain a high frequency band signal of the current frame.

  For example, the amplitude of the high frequency band signal of the current frame may be adjusted according to the overall gain, or the amplitude of the high frequency band signal of the subframe may be adjusted according to the subframe gain.

  In this embodiment of the present invention, when it is determined that the current frame is a lost frame, the subframe gain of the subframe before the current frame and the gain gradient between the subframes before the current frame The subframe gain of the subframe of the current frame is determined according to, and the high frequency band signal is adjusted using the determined subframe gain of the current frame. The subframe gain of the current frame is obtained according to the gradient (which is the trend and degree of change) between the subframe gains of subframes prior to the current frame, so that the transition before and after frame loss is more continuous This reduces noise during signal reconstruction and improves call quality.

  According to this embodiment of the invention, at 120, the subframe gain of the starting subframe of the current frame is determined according to the subframe gain of the subframe of at least one frame and the gain gradient between the subframes of at least one frame. Determine the subframe gain of the other subframes excluding the start subframe of at least two subframes, the subframe gain of the start subframe of the current frame and the gain gradient between the subframes of at least one frame Determine according to

  According to this embodiment of the present invention, at 120, the first gain gradient between the last subframe of the previous frame of the current frame and the starting subframe of the current frame is Is estimated according to the gain gradient between the subframes of the current frame, the subframe gain of the start subframe of the current frame is estimated according to the subframe gain of the last subframe of the previous frame of the current frame and the first gain gradient, and the current Subframes of other subframes, excluding the start subframe of at least two subframes, estimating the gain gradient between at least two subframes of the frame according to the gain gradient between the subframes of at least one frame Gain is the gain slope between at least two subframes of the current frame And estimating accordance with the sub-frame gains starting subframe of the current frame.

  According to this embodiment of the present invention, the gain gradient between the last two subframes of the previous frame can be used as the estimated value of the first gain gradient. The present embodiment of the present invention is not limited to this, and weighted averaging can be performed on the gain gradient between a plurality of subframes of the previous frame to obtain an estimated value of the first gain gradient.

  For example, an estimate of the gain gradient between two adjacent subframes of the current frame is the weight of the gain gradient between two subframes corresponding to the position of the two adjacent subframes in the previous frame of the current frame. May be a gain gradient between two subframes corresponding to the position of two adjacent subframes in the previous frame of the previous frame of the previous average of the current frame, or two adjacent in the current frame The estimate of gain gradient between subframes may be a weighted average of gain gradients between several adjacent subframes prior to two adjacent subframes of the previous subframe.

  For example, in a situation where the gain gradient between two subframes points to the difference between the gains of the two subframes, the estimated subframe gain for the start subframe of the current frame is the last subframe of the previous frame. It may be the sum of the subframe gain of the frame and the first gain gradient. If the gain gradient between two subframes points to the subframe gain attenuation factor between the two subframes, the subframe gain of the start subframe of the current frame is the subframe gain of the last subframe of the previous frame. It may be the product of the frame gain and the first gain gradient.

  At 120, weighted averaging is performed on a gain gradient between at least two subframes of the previous frame of the current frame to obtain a first gain gradient, when performing weighted averaging, The gain gradient between subframes of the previous frame of the current frame close to the current frame occupies a greater weight, gets the subframe gain of the start subframe of the current frame, and the previous frame of the current frame Subframe gain and first gain slope of the last subframe of the current frame, as well as the type of last frame received before the current frame (or referred to as the frame class of the last normal frame) and consecutive lost before the current frame Estimate according to the number of frames.

  For example, in situations where the gain gradient between subframes of the previous frame is monotonically increasing or monotonically decreasing, weighted averaging can be performed using two gain gradients between the last three subframes of the previous frame. (The gain gradient between the third subframe from the last and the second subframe from the last and the gain gradient between the second subframe from the last and the last subframe) to obtain the first gain gradient May be. In situations where the gain gradient between subframes of the previous frame is neither monotonically increasing nor monotonically decreasing, weighted averaging may be performed on the gain gradients between all adjacent subframes in the previous frame. Two adjacent subframes prior to the current frame close to the current frame show a stronger correlation between the audio signal transmitted in the two adjacent subframes and the audio signal transmitted in the current frame. In this case, the gain gradient between adjacent subframes may be close to the actual value of the first gain gradient. Therefore, when estimating the first gain gradient, the weight occupied by the gain gradient between subframes in the previous frame close to the current frame can be set to a larger value. In this way, the estimated value of the first gain gradient can be close to the actual value of the first gain gradient, so that the transition before and after frame loss is more continuous, thereby improving the call quality doing.

  According to this embodiment of the present invention, in the process of estimating the subframe gain, the estimated gain is determined according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. Can be adjusted. In particular, the gain gradient between the subframes of the current frame can be estimated first, and then the final condition before the current frame as a decision condition in relation to the subframe gain of the last subframe of the previous frame of the current frame Using the frame class of the normal frame and the number of consecutive lost frames before the current frame, the subframe gain of all subframes of the current frame is estimated using the gain gradient between the subframes.

  For example, the frame class of the last frame received before the current frame may refer to the frame class of the nearest normal frame (not the lost frame) received before the current frame and received by the decoder. For example, the encoder side sends four frames to the decoder side, where the decoder side correctly receives the first frame and the second frame and loses the third frame and the fourth frame. As a result, the last normal frame before the frame loss may point to the second frame. In general, the frame type is (1) a frame with one of the features of unvoiced, silent, noise, and voiced termination (UNVOICED_CLAS frame), (2) unvoiced, where the voiced sound is the beginning but is relatively weak Transition frame from voice to sound (UNVOICED_TRANSITION frame), (3) Frame of transition after voiced sound (VOICED_TRANSITION frame), (4) Frame before this frame Is a voiced frame or a voiced start frame with a voiced sound feature (VOICED_CLAS frame), (5) a start frame with an obvious voiced sound (ONSET frame), (6) a start frame with mixed harmonics and noise (SIN_ONSET frame) and (7) a frame having an inactive feature (INACTIVE_CLAS frame).

  The number of consecutive lost frames may refer to the number of consecutive lost frames after the last normal frame, or may refer to the rank of the current lost frame within the consecutive lost frames. For example, it is assumed that the encoder side transmits five frames to the decoder side, the decoder side correctly receives the first frame and the second frame, and has lost the fifth frame from the third frame. If the current lost frame is the fourth frame, the number of consecutive lost frames is 2, and if the current lost frame is the fifth frame, the number of consecutive lost frames is 3.

  For example, the frame class of the current frame (which is a lost frame) is the same as the frame class of the last frame received before the current frame, and the number of consecutive current frames is less than or equal to a threshold (eg, 3) In some situations, the estimate of the gain slope between subframes of the current frame is close to the actual value of the gain slope between subframes of the current frame, otherwise the gain between subframes of the current frame The estimate of the slope is far from the actual value of the gain slope between subframes of the current frame. Therefore, the estimated gain gradient between subframes of the current frame is the last frame received before the current frame so that the adjusted gain gradient between subframes of the current frame is close to the actual value of the gain gradient. May be adjusted according to the current frame class and the number of consecutive current frames, so that transitions before and after frame loss are more continuous, thereby improving call quality.

  For example, if the number of consecutive lost frames is less than the threshold and the decoder determines that the final normal frame is the start frame or unvoiced frame of the voiced frame, the current frame can also be a voiced or unvoiced frame. It may be determined. That is, the frame class of the current frame is received before the current frame, using the frame class of the last normal frame before the current frame and the number of consecutive lost frames before the current frame as decision criteria It may be determined whether it is the same as the frame class of the last frame, or if the frame class of the current frame is the same as the frame class of the last frame received before the current frame, the gain factor is If the current frame is adjusted to take a relatively large value and the frame class of the current frame is different from the frame class of the last frame received before the current frame, the gain factor takes a relatively small value. To be adjusted.

GainGradFEC[0]は、第1のゲイン勾配であり、GainGrad[n-1,j]は、現在のフレームの前回のフレームの第jのサブフレームと第(j+1)のサブフレームとの間のゲイン勾配であり、α_j+1≧α_jであり、

であり、j=0、1、2、・・・、I-2であり、
開始サブフレームのサブフレームゲインは、以下の式(2)および(3)を使用して取得され、
GainShapeTemp[n,0]=GainShape[n-1,I-1]+φ₁*GainGradFEC[0] (2)
GainShape[n,0]=GainShapeTemp[n,0]*φ₂ (3)
GainShape[n-1,I-1]は、第(n-1)のフレームの第(I-1)のサブフレームのサブフレームゲインであり、GainShape[n,0]は、現在のフレームの開始サブフレームのサブフレームゲインであり、GainShapeTemp[n,0]は、開始サブフレームのサブフレームゲインの中間値であり、0≦φ₁≦1.0であり、0<φ₂≦1.0であり、φ₁は、現在のフレームの前に受信した最終フレームのフレームクラスおよび第1のゲイン勾配の正または負の符号を使用して決定され、φ₂は、現在のフレームの前に受信した最終フレームのフレームクラスおよび現在のフレームより前の連続したロストフレームの数を使用して決定される。 According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes. In the case, the first gain slope is obtained using the following equation (1):

GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, j] is between the jth subframe and the (j + 1) th subframe of the previous frame of the current frame. Gain gradient, α _{j + 1} ≧ α _j ,

J = 0, 1, 2, ..., I-2,
The subframe gain of the starting subframe is obtained using the following equations (2) and (3):
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + φ ₁ * GainGradFEC [0] (2)
GainShape [n, 0] = GainShapeTemp [n, 0] * φ ₂ (3)
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the (n-1) th frame, and GainShape [n, 0] is the start of the current frame The subframe gain of the subframe, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 ≦ φ ₁ ≦ 1.0, 0 <φ ₂ ≦ 1.0, φ ₁ Is determined using the frame class of the last frame received before the current frame and the positive or negative sign of the first gain gradient, and φ ₂ is the frame of the last frame received before the current frame Determined using class and number of consecutive lost frames prior to current frame.

For example, if the frame class of the last frame received before the current frame is a voiced or unvoiced frame, if the first gain slope is positive, the value of φ ₁ is relatively small, for example, it is less than the threshold set in advance, also, if the first gain slope negative, phi ₁ values are relatively large, e.g., greater than a threshold set in advance.

For example, if the frame class of the last frame received before the current frame is the start frame or unvoiced frame of a voiced frame, the value of φ ₁ is relatively large if the first gain gradient is positive. For example, if it becomes larger than a preset threshold value and the first gain gradient is negative, the value of φ ₁ is relatively small, for example, less than the preset threshold value.

For example, if the frame class of the last frame received before the current frame is a voiced or unvoiced frame and the number of consecutive lost frames is 3 or less, the value of φ ₂ is relatively small, for example , Less than a preset threshold.

For example, if the frame class of the last frame received before the current frame is the start frame of a voiced frame or the start frame of an unvoiced frame, and the number of consecutive lost frames is 3 or less, the value of φ ₂ is It is relatively large, for example, larger than a preset threshold value.

For example, for the same type of frame, φ ₂ indicates a larger value as the number of consecutive lost frames decreases.

  At 120, the gain gradient between the subframe before the last subframe of the previous frame of the current frame and the last subframe of the previous frame of the current frame is used as the first gain gradient, The subframe gain of the start subframe of the frame, the subframe gain and first gain gradient of the last subframe of the previous frame of the current frame, and the frame class of the last frame received before the current frame and the current Estimate according to the number of consecutive lost frames before the frame.

According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes. In the case, the first gain slope is obtained using equation (4) below:
GainGradFEC [0] = GainGrad [n-1, I-2] (4)
GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, I-2] is the (I-1) th subframe of the previous (I-1) th subframe and the current frame. And the gain gradient between the subframes of
The subframe gain of the starting subframe is obtained using the following equations (5), (6), and (7):
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + λ ₁ * GainGradFEC [0] (5)
GainShapeTemp [n, 0] = min (λ ₂ * GainShape [n-1, I-1], GainShapeTemp [n, 0]) (6)
GainShape [n, 0] = max (λ ₃ * GainShape [n-1, I-1], GainShapeTemp [n, 0]) (7)
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the previous frame of the current frame, and GainShape [n, 0] is the subframe of the start subframe Gain, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 <λ ₁ <1.0, 1 <λ ₂ <2, 0 <λ ₃ <1.0 Yes, λ ₁ is determined using multiple relationships between the frame class of the last frame received before the current frame and the subframe gains of the last two subframes of the previous frame of the current frame, λ ₂ and λ ₃ are determined using the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame.

For example, if the frame class of the last frame received before the current frame is a voiced or unvoiced frame, the current frame can also be a voiced or unvoiced frame. In this case, the larger the ratio of the subframe gain of the last subframe in the previous frame to the subframe gain of the second subframe from the last, the larger λ ₁ becomes, and the subframe gain of the second subframe from the last increases. Indeed lambda ₁ smaller the ratio of the sub-frame gains of the last subframe in the previous frame relative to the frame gain indicates a small value. Moreover, lambda ₁ of values when the frame class of the last frame received is unvoiced frame before the current frame, lambda ₁ in the case frame class of the last frame received before the current frame is a voiced frame Greater than the value of.

For example, if the frame class of the last normal frame is an unvoiced frame and the number of consecutive lost frames is 1 at the present time, the current lost frame follows the last normal frame, and the lost frame, the last normal frame, There is a very strong correlation between them, it can be determined that the energy of the lost frame is relatively close to the energy of the final normal frame, and the values of λ ₂ and λ ₃ can be close to 1. For example, the value of λ ₂ can be 1.2 and the value of λ ₃ can be 0.8.

  At 120, weighted averaging is performed on the gain gradient between the i-th and (i + 1) -th subframes of the previous frame of the current frame and the previous frame of the previous frame of the current frame. The gain gradient between the i-th subframe and the (i + 1) -th subframe is performed, and the gain gradient between the i-th subframe and the (i + 1) -th subframe of the current frame is calculated. Where i = 0, 1,..., I-2, and the i-th subframe and (i + 1) -th subframe of the previous frame of the current frame The weight occupied by the gain gradient between is greater than the weight occupied by the gain gradient between the i-th subframe of the previous frame and the (i + 1) -th subframe of the previous frame of the current frame, Starting subframe of at least two subframes The subframe gain of the other subframes excluding, the gain gradient between at least two subframes of the current frame, the subframe gain of the start subframe of the current frame, and the last frame received before the current frame Estimate according to the frame class of and the number of consecutive lost frames before the current frame.

  According to this embodiment of the present invention, at 120, the weighted averaging is performed with the gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame of the current frame and the current Perform a gain gradient between the i-th subframe and (i + 1) -th subframe of the previous frame of the previous frame, and the i-th subframe and (i + 1) -th of the current frame , Where i = 0, 1,..., I-2, and the i-th subframe and the i-th subframe of the previous frame of the current frame. The weight occupied by the gain gradient between (i + 1) subframes is between the i th subframe of the previous frame and the (i + 1) subframe of the previous frame of the current frame. Is greater than the weight occupied by the gain gradient of Subframe gains of the other subframes except the starting subframe of the current frame, the gain gradient between at least two subframes of the current frame, the subframe gain of the starting subframe of the current frame, and the current You may estimate according to the frame class of the last frame received before the frame and the number of consecutive lost frames before the current frame.

According to this embodiment of the present invention, if the previous frame of the current frame is the (n-1) th frame and the current frame is the nth frame, at least two of the current frame The gain gradient between subframes is determined using the following equation (8):
GainGradFEC [i + 1] = GainGrad [n-2, i] * β ₁ + GainGrad [n-1, i] * β ₂ (8)
GainGradFEC [i + 1] is the gain gradient between the i-th subframe and the (i + 1) -th subframe, and GainGrad [n-2, i] is the previous frame of the current frame. Gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, and GainGrad [n−1, i] is the i-th subframe of the previous frame of the current frame. Gain gradient between the frame and the (i + 1) th subframe, β ₂ > β ₁ , β ₂ + β ₁ = 1.0, i = 0, 1, 2,. I-2,
Subframe gains of other subframes excluding the start subframe of at least two subframes are determined using the following equations (9) and (10):
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] * β ₃ (9)
GainShape [n, i] = GainShapeTemp [n, i] * β ₄ (10)
GainShape [n, i] is the subframe gain of the i-th subframe of the current frame, and GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame 0 ≦ β ₃ ≦ 1.0, 0 <β ₄ ≦ 1.0, and β ₃ is a plurality of relationships between GainGrad [n−1, i] and GainGrad [n−1, i + 1] and Determined using the positive or negative sign of GainGrad [n-1, i + 1], where β ₄ is the frame class of the last frame received before the current frame and consecutive lost before the current frame Determined using the number of frames.

For example, when GainGrad [n−1, i + 1] is a negative value, β ₃ increases as the ratio of GainGrad [n−1, i + 1] to GainGrad [n−1, i] increases. Indicates a large value, or when GainGradFEC [0] is a negative value, the larger the ratio of GainGrad [n-1, i + 1] to GainGrad [n-1, i], the larger β ₃ Indicates a small value.

For example, if the frame class of the last frame received before the current frame is a voiced or unvoiced frame and the number of consecutive lost frames is 3 or less, the value of β ₄ is relatively small, for example , Less than a preset threshold.

For example, if the frame class of the last frame received before the current frame is the start frame of a voiced frame or the start frame of an unvoiced frame, and the number of consecutive lost frames is 3 or less, the value of β ₄ is It is relatively large, for example, larger than a preset threshold value.

For example, for the same type of frame, β ₄ shows a larger value as the number of consecutive lost frames becomes smaller.

According to this embodiment of the present invention, each frame includes I subframes and estimates a gain gradient between at least two subframes of the current frame according to a gain gradient between subframes of at least one frame. The steps are
Performing weighted averaging on I gain gradients between (I + 1) subframes prior to the i-th subframe of the current frame, and the i-th subframe and (( i + 1) is a step of estimating a gain gradient between subframes, i = 0, 1,..., I-2, and a gain gradient between subframes close to the i-th subframe. Includes steps that occupy a greater weight,
Estimate subframe gains of other subframes excluding the start subframe of at least two subframes according to the gain gradient between at least two subframes of the current frame and the subframe gain of the start subframe of the current frame The steps to do are
The gain gradient between at least two subframes of the current frame and the subframe gain of the starting subframe of the current frame, as well as the frame class of the last frame received before the current frame and consecutive lost before the current frame Estimating a subframe gain of other subframes excluding a start subframe of at least two subframes according to the number of frames.

According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes four subframes. The gain slope between at least two subframes of the current frame is determined using the following equations (11), (12), and (13):
GainGradFEC [1] = GainGrad [n-1,0] * γ ₁ + GainGrad [n-1,1] * γ ₂
+ GainGrad [n-1,2] * γ ₃ + GainGradFEC [0] * γ ₄ (11)
GainGradFEC [2] = GainGrad [n-1,1] * γ ₁ + GainGrad [n-1,2] * γ ₂
+ GainGradFEC [0] * γ ₃ + GainGradFEC [1] * γ ₄ (12)
GainGradFEC [3] = GainGrad [n-1,2] * γ ₁ + GainGradFEC [0] * γ ₂
+ GainGradFEC [1] * γ ₃ + GainGradFEC [2] * γ ₄ (13)
GainGradFEC [j] is the gain gradient between the jth and (j + 1) th subframe of the current frame, and GainGrad [n-1, j] is the previous gradient of the current frame. Is the gain gradient between the jth subframe and the (j + 1) th subframe of the frame, j = 0, 1, 2,..., I-2, and γ ₁ + γ ₂ + γ ₃ + γ ₄ = 1.0, γ ₄ > γ ₃ > γ ₂ > γ ₁ and γ ₁ , γ ₂ , γ ₃ , and γ ₄ are determined using the frame class of the last frame received And
Subframe gains of other subframes excluding the start subframe of at least two subframes are determined using the following equations (14), (15), and (16):
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] (14)
Where i = 1, 2, and 3, where GainShapeTemp [n, 0] is the first gain gradient,
GainShapeTemp [n, i] = min (γ ₅ * GainShape [n-1, i], GainShapeTemp [n, i]) (15)
GainShape [n, i] = max (γ ₆ * GainShape [n-1, i], GainShapeTemp [n, i]) (16)
i = 1, 2, and 3, GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame, and GainShape [n, i] is the subframe gain of i subframes, γ ₅ and γ ₆ are determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame, where 1 <γ ₅ < 2 and 0 ≦ γ ₆ ≦ 1.

For example, if the frame class of the last normal frame is an unvoiced frame and the number of consecutive lost frames is 1 at the present time, the current lost frame follows the last normal frame, and the lost frame, the last normal frame, There is a very strong correlation between, and it can be determined that the energy of the lost frame is relatively close to the energy of the final normal frame, and the values of γ ₅ and γ ₆ can be close to 1. For example, the value of γ ₅ can be 1.2, and the value of γ ₆ can be 0.8.

  At 130, the overall gain gradient of the current frame is estimated according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame, Is estimated according to the overall gain gradient and the overall gain of the previous frame of the current frame.

  For example, during estimation of the overall gain, the overall gain of the lost frame is based on the overall gain of at least one frame prior to the current frame (e.g., the previous frame). May be estimated using conditions such as the frame class of the last frame received and the number of consecutive lost frames prior to the current frame.

According to this embodiment of the invention, the overall gain of the current frame is determined using the following equation (17):
GainFrame = GainFrame_prevfrm * GainAtten (17)
GainFrame is the overall gain of the current frame, GainFrame_prevfrm is the overall gain of the previous frame of the current frame, 0 <GainAtten ≦ 1.0, and GainAtten is the overall gain gradient , GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame.

  For example, in a situation where the decoder determines that the frame class of the current frame is the same as the frame class of the last frame received before the current frame and the number of consecutive lost frames is 3 or less, the decoder The side may determine that the overall gain slope is unity. That is, the overall gain of the current lost frame can be the same as the overall gain of the previous frame, so the overall gain gradient may be determined to be 1.

  For example, if the final normal frame is an unvoiced or voiced frame and the number of consecutive lost frames can be determined to be 3 or less, the decoder side has a relatively small value for the overall gain gradient. That is, it can be determined that the overall gain gradient can be less than a preset threshold. For example, the threshold value may be set to 0.5.

  For example, in situations where the decoder side determines that the final normal frame is the start frame of a voiced frame, the decoder side can determine the overall gain gradient, so that the overall gain gradient is preset. It becomes larger than the first threshold value. If the final normal frame is determined to be the start frame of the voiced frame, the decoder side may determine that the current lost frame can be very likely a voice frame, and then the overall gain gradient is It can be determined that a relatively large value, i.e. the overall gain gradient, can be greater than a preset threshold.

  According to this embodiment of the invention, in situations where the decoder side determines that the final normal frame is the start frame of an unvoiced frame, the decoder side can determine the overall gain gradient, so that the overall The typical gain gradient is less than a preset threshold. For example, if the last normal frame is the start frame of an unvoiced frame, the current lost frame can be very likely an unvoiced frame, and then the decoder side compares the overall gain gradient May be determined to be small, i.e., the overall gain slope may be less than a preset threshold.

  In this embodiment of the present invention, the subframe gain gradient and overall gain gradient are estimated using states such as the frame class of the last frame received before the frame loss occurred and the number of consecutive lost frames. And then determine the subframe gain and overall gain for the current frame relative to at least one previous frame subframe gain and overall gain, and gain control using two gains This is performed on the reconstructed high frequency band signal, and the final high frequency band signal is output. In this embodiment of the present invention, when a frame loss occurs, a fixed value is not used as a subframe gain and an overall gain value required during decoding, thereby causing a frame loss. Avoiding signal energy discontinuity due to setting a fixed gain value in the situation, resulting in a more natural and more stable transition before and after frame loss, thereby reducing noise and reconstructing The signal quality has been improved.

  FIG. 2 is a schematic flowchart of a decoding method according to another embodiment of the present invention. The method in FIG. 2 is performed by a decoder and includes:

  210: If it is determined that the current frame is a lost frame, a high frequency band signal is synthesized according to the decoding result of the previous frame of the current frame.

  220: Determine the subframe gain of at least two subframes of the current frame.

  230: Estimate the overall gain gradient of the current frame according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame.

  240: Estimate the overall gain of the current frame according to the overall gain gradient and the overall gain of the previous frame of the current frame.

  250: Adjust the synthesized high frequency band signal according to the overall gain and the subframe gain of at least two subframes to obtain the high frequency band signal of the current frame.

According to this embodiment of the present invention, the overall gain of the current frame is
Determined using GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the overall gain of the current frame, GainFrame_prevfrm is the overall gain of the previous frame of the current frame, and 0 <GainAtten ≦ 1.0 Yes, GainAtten is the overall gain gradient, and GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame.

  3A to 3C are schematic diagrams of trends in changes in subframe gain of the previous frame according to an embodiment of the present invention. FIG. 4 is a schematic diagram of a process for estimating a first gain gradient according to an embodiment of the present invention. FIG. 5 is a schematic diagram of a process for estimating a gain gradient between at least two subframes of a current frame according to an embodiment of the present invention. FIG. 6 is a schematic flowchart of a decoding process according to an embodiment of the present invention. This embodiment in FIG. 6 is an illustration of the method in FIG.

  610: The decoder side analyzes the information about the bitstream received by the encoder side.

  615: It is determined whether or not a frame loss has occurred according to the frame loss flag analyzed from the information about the bitstream.

  620: If no frame loss has occurred, normal decoding processing is performed according to the bit stream parameters acquired from the bit stream.

  During decoding, first, dequantization is performed on the LSF parameters, subframe gain, and overall gain, and the LSF parameters are converted to LPC parameters, thereby obtaining an LPC synthesis filter, and second In addition, the core decoder is used to obtain parameters such as pitch period, algebraic codebook, and respective gain, and the high frequency band excitation signal is converted into parameters such as pitch period, algebraic codebook, and respective gain. And using LPC synthesis filters to synthesize the high frequency band signal from the high frequency band excitation signal, and finally perform gain adjustment on the high frequency band signal according to the subframe gain and overall gain, and finally A typical high frequency band signal.

  If a frame loss has occurred, a frame loss process is performed. The frame loss process includes steps 625 to 660.

  625: Use the core decoder to get parameters such as pitch period, algebraic codebook, and previous gain of each frame, and based on parameters such as pitch period, algebraic codebook, and respective gain, Acquire a high frequency band excitation signal.

  630: Duplicate LPC parameter of previous frame.

  635: An LPC synthesis filter is acquired according to the LPC of the previous frame, and a high frequency band signal is synthesized from the high frequency band excitation signal using the LPC synthesis filter.

  640: Estimate a first gain gradient from the last subframe of the previous frame to the start subframe of the current frame according to the gain gradient between the subframes of the previous frame.

In the present embodiment, description will be made using an example in which each frame is within the total gain of four subframes. Assume that the current frame is the nth frame, that is, the nth frame is a lost frame. The previous subframe is the (n-1) th subframe, and the previous frame of the previous frame is the (n-2) th frame. The gains of the four subframes of the nth frame are GainShape [n, 0], GainShape [n, 1], GainShape [n, 2], and GainShape [n, 3]. Similarly, the gains of the four subframes of the (n-1) th frame are GainShape [n-1,0], GainShape [n-1,1], GainShape [n-1,2], and GainShape [ n-1,3] and the gains of the four subframes of the (n-2) th frame are GainShape [n-2,0], GainShape [n-2,1], GainShape [n-2, 2], and GainShape [n-2,3]. In this embodiment of the invention, a different estimation algorithm is used to calculate the subframe gain GainShape [n, 0] of the first subframe of the nth frame (ie, the subframe of the current frame whose serial number is 0). Gain) and subframe gain for the next three subframes. The procedure for estimating the subframe gain GainShape [n, 0] of the first subframe calculates the gain change according to the trend and degree of change between the subframe gains of the (n−1) th frame, Subframe gain GainShape [n, 0] of the subframe of the second subframe, gain change and gainShape [n-1,3] of the fourth subframe of the (n-1) th frame (that is, its serial number is 3 Is used to estimate the frame class of the last frame received before the current frame and the number of consecutive lost frames. The estimation procedure for the next three subframes determines the gain change as the trend and degree of change between the subframe gain of the (n-1) th frame and the subframe gain of the (n-2) th frame. The frame of the last frame received before the current frame using the gain change and the estimated subframe gain of the first subframe of the nth subframe Estimates related to class and number of consecutive lost frames.

As shown in FIG. 3A, the trend and degree (or gradient) of the change between the gains of the (n−1) th frame is monotonically increasing. As shown in FIG. 3B, the trend and degree (or slope) of change between gains in the (n−1) th frame is monotonously decreasing. The equation for calculating the first gain gradient may be as follows.
GainGradFEC [0] = GainGrad [n-1,1] * α ₁ + GainGrad [n-1,2] * α ₂
Here, GainGradFEC [0] is the first gain gradient, that is, the gain gradient between the last subframe of the (n-1) th frame and the first subframe of the nth frame, and GainGrad [n−1,1] is a gain gradient between the first subframe and the second subframe of the (n−1) th subframe, α ₂ > α ₁ , α ₁ + α ₂ = 1, ie, the gain gradient between subframes close to the nth subframe occupies a greater weight. For example, α ₁ = 0.1 and α ₂ = 0.9.

As shown in FIG. 3C, the trend and degree (or slope) of change between gains in the (n−1) th frame is not monotonic (eg, random). The equation for calculating the gain gradient may be as follows:
GainGradFEC [0] = GainGrad [n-1,0] * α ₁ + GainGrad [n-1,1] * α ₂ + GainGrad [n-1,2] * α ₃
Here, α ₃ > α ₂ > α ₁ and α ₁ + α ₂ + α ₃ = 1.0, that is, the gain gradient between subframes close to the nth subframe occupies a greater weight. For example, α ₁ = 0.2, α ₂ = 0.3, and α ₃ = 0.5.

  645: Estimate the subframe gain of the start subframe of the current frame according to the subframe gain of the last subframe of the previous frame and the first gain gradient.

In this embodiment of the present invention, an intermediate amount GainShapeTemp [n, 0] of the subframe gain GainShape [n, 0] of the first subframe of the nth frame is received last before the nth frame. The calculation may be performed according to the frame class of the frame and the first gain gradient GainGradFEC [0]. Specific steps are as follows.
GainShapeTemp [n, 0] = GainShape [n-1,3] + φ ₁ * GainGradFEC [0]
Here, 0 ≦ φ ₁ ≦ 1.0, and φ ₁ is determined using the frame class of the last frame received before the nth frame and the positive or negative value of GainGradFEC [0]. .

GainShape [n, 0] is obtained by calculation according to the intermediate amount GainShapeTemp [n, 0].
GainShape [n, 0] = GainShapeTemp [n, 0] * φ ₂
Here, φ ₂ is determined using the frame class of the last frame received before the nth frame and the number of consecutive lost frames before the nth frame.

  650: Estimate gain gradient between subframes of current frame according to gain gradient between subframes of at least one frame, gain gradient between subframes of current frame and start subframe of current frame The subframe gains of the other subframes excluding the start subframe of the plurality of subframes are estimated according to the subframe gain of the subframe.

Referring to FIG. 5, in the present embodiment of the present invention, the gain gradient GainGradFEC [i + 1] between at least two subframes of the current frame is calculated between the subframes of the (n−1) th frame. The estimation may be performed according to the gain gradient and the gain gradient between subframes of the (n−2) th frame.
GainGradFEC [i + 1] = GainGrad [n-2, i] * β ₁ belta1 + GainGrad [n-1, i] * β ₂
Here, i = 0, 1, 2 and β ₁ + β ₂ = 1.0, that is, the gain gradient between subframes close to the nth subframe is, for example, β ₁ = 0.4, and β ₂ Occupies a greater weight, such as = 0.6.

The intermediate amount GainShapeTemp [n, i] of the subframe gain of the subframe is calculated according to the following equation.
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] * β ₃
Where i = 1, 2, 3 and 0 ≦ β ₃ ≦ 1.0, and β ₃ can be determined using GainGrad [n−1, x], for example GainGrad [n−1, When 2] is greater than 10.0 * GainGrad [n-1,1] and GainGrad [n-1,1] is greater than 0, the value of β ₃ is 0.8.

The subframe gain of the subframe is calculated according to the following equation.
GainShape [n, i] = GainShapeTemp [n, i] * β ₄
Where i = 1, 2, 3 and β ₄ is determined using the frame class of the last frame received before the nth frame and the number of consecutive lost frames before the nth frame. Is done.

  655: Estimate the overall gain gradient according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame.

  The overall gain gradient GainAtten can be determined according to the frame class of the last frame received before the current frame and the number of consecutive lost frames, where 0 <GainAtten <1.0. For example, the basic principle of determining the overall gain slope is that if the frame class of the last frame received before the current frame is a friction sound, the overall gain slope is close to 1, eg GainAtten. It may be set to take 0.95. For example, if the number of consecutive lost frames is greater than 1, the overall gain gradient takes a relatively small value (eg, close to 0), eg, GainAtten = 0.5.

660: Estimate the overall gain of the current frame according to the overall gain gradient and the overall gain of the previous frame of the current frame. The overall gain of the current lost frame may be obtained using the following equation:
GainFrame = GainFrame_prevfrm * GainAtten, where GainFrame_prevfrm is the overall gain of the previous frame.

  665: Perform gain adjustment on the synthesized high frequency band signal according to the overall gain and subframe gain, thereby recovering the high frequency band signal of the current frame. This step is similar to conventional techniques, and details thereof are not described herein again.

  In this embodiment of the present invention, the conventional frame loss processing method in the time domain high bandwidth extension technology is used, and as a result, the transition when a frame loss occurs becomes more natural and more stable. Thereby, the noise (click) phenomenon caused by the frame loss is reduced, and the quality of the audio signal is improved.

  If necessary, as another embodiment, 640 and 645 in this embodiment in FIG. 6 may be replaced with the following steps.

  First step: change gradient GainGrad [n-1, from the subframe gain of the second subframe to the subframe gain of the last subframe in the (n-1) th frame (which is the previous frame) 2] is used as the first gain gradient GainGradFEC [0], ie GainGradFEC [0] = GainGrad [n−1,2].

Second step: Based on the subframe gain of the last subframe of the (n-1) th frame, the frame class of the last frame received before the current frame and the first gain gradient GainGradFEC [0] Thus, an intermediate amount GainShapeTemp [n, 0] of the gain GainShape [n, 0] of the first subframe is calculated.
GainShapeTemp [n, 0] = GainShape [n-1,3] + λ ₁ * GainGradFEC [0]
Here, GainShape [n−1,3] is the gain of the fourth subframe of the (n−1) th frame, 0 <λ ₁ <1.0, and λ ₁ is the nth frame It is determined using a plurality of relationships between the frame class of the last frame received previously and the gains of the last two subframes of the previous frame.

Third step: GainShape [n, 0] is obtained by calculation according to the intermediate amount GainShapeTemp [n, 0].
GainShapeTemp [n, 0] = min (λ ₂ * GainShape [n-1,3], GainShapeTemp [n, 0]),
GainShape [n, 0] = max (λ ₃ * GainShape [n-1,3], GainShapeTemp [n, 0])
Where λ ₂ and λ ₃ are determined using the frame class of the last frame received before the current frame and the number of consecutive lost frames, and the last subframe of the (n−1) th frame. The ratio of the estimated subframe gain GainShape [n, 0] of the first subframe to the subframe gain GainShape [n−1,3] is within a certain range.

  If necessary, as another embodiment, 550 in this embodiment in FIG. 5 may be replaced with the following steps.

First step: Predict and estimate the gain gradient GainGradFEC [1] for GainGradFEC [3] between subframes of the nth frame according to GainGrad [n−1, x] and GainGradFEC [0].
GainGradFEC [1] = GainGrad [n-1,0] * γ ₁ + GainGrad [n-1,1] * γ ₂
+ GainGrad [n-1,2] * γ ₃ + GainGradFEC [0] * γ ₄ ,
GainGradFEC [2] = GainGrad [n-1,1] * γ ₁ + GainGrad [n-1,2] * γ ₂
+ GainGradFEC [0] * γ ₃ + GainGradFEC [1] * γ ₄ ,
GainGradFEC [3] = GainGrad [n-1,2] * γ ₁ + GainGradFEC [0] * γ ₂
+ GainGradFEC [1] * γ ₃ + GainGradFEC [2] * γ ₄
Where γ ₁ + γ ₂ + γ ₃ + γ ₄ = 1.0, γ ₄ > γ ₃ > γ ₂ > γ ₁ , and γ ₁ , γ ₂ , γ ₃ , and γ ₄ are the current frame Is determined using the frame class of the last frame received before.

Second step: An intermediate amount GainShapeTemp [n, 1] with respect to GainShapeTemp [n, 3] of the subframe gain GainShape [n, 1] with respect to GainShape [n, 3] between subframes of the nth frame is calculated.
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i]
Here, i = 1, 2, and 3, and GainShapeTemp [n, 0] is the subframe gain of the first subframe of the nth frame.

Third step: A subframe gain GainShape [n, 1] for GainShape [n, 3] between subframes of the nth frame is calculated according to an intermediate amount GainShapeTemp [n, 1] for GainShapeTemp [n, 3].
GainShapeTemp [n, i] = min (γ ₅ * GainShape [n-1, i], GainShapeTemp [n, i]),
GainShape [n, i] = max (γ ₆ * GainShape [n-1, i], GainShapeTemp [n, i])
Where i = 1, 2, 3 and γ ₅ and γ ₆ use the frame class of the last frame received before the nth frame and the number of consecutive lost frames before the nth frame To be determined.

  FIG. 7 is a schematic structural diagram of a decoding device 700 according to an embodiment of the present invention. The decoding device 700 includes a generation module 710, a determination module 720, and an adjustment module 730.

  The generation module 710 is configured to synthesize the high frequency band signal according to the decoding result of the previous frame of the current frame in situations where the current frame is determined to be a lost frame. The determination module 720 determines a subframe gain of at least two subframes of the current frame according to a subframe gain of a subframe of at least one frame prior to the current frame and a gain gradient between the subframes of at least one frame. And is configured to determine the overall gain of the current frame. The adjustment module 730 obtains the high frequency band signal of the current frame, and the high frequency band synthesized by the generation module according to the overall gain and subframe gain of at least two subframes determined by the determination module. Configured to condition the signal.

  According to this embodiment of the invention, the determination module 720 determines the subframe gain of the starting subframe of the current frame according to the subframe gain of the subframe of at least one frame and the gain gradient between the subframes of at least one frame. Subframe gain of other subframes excluding the start subframe of at least two subframes according to the subframe gain of the start subframe of the current frame and the gain gradient between the subframes of at least one frame To decide.

  According to this embodiment of the present invention, the determination module 720 determines the last subframe of the previous frame of the current frame and the start subframe of the current frame according to the gain gradient between the subframes of the previous frame of the current frame. Estimate the first gain gradient between and the subframe gain of the last subframe of the previous frame of the current frame and the subframe gain of the start subframe of the current frame according to the first gain gradient, and at least Estimate the gain gradient between at least two subframes of the current frame according to the gain gradient between the subframes of one frame, and the gain gradient between at least two subframes of the current frame and the starting subframe of the current frame Over at least two subframes according to subframe gain Estimating the sub-frame gains of other sub-frames excluding the start subframe.

  According to this embodiment of the invention, the determination module 720 performs weighted averaging on the gain gradient between at least two subframes of the previous frame of the current frame to obtain a first gain gradient, and According to the subframe gain and first gain gradient of the last subframe of the previous frame of the current frame, and the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame Estimate the subframe gain of the start subframe of the current frame, where the weighted averaging gives a greater weight to the gain gradient between subframes of the previous frame of the current frame close to the current frame Occupy.

を使用して取得され、GainGradFEC[0]は、第1のゲイン勾配であり、GainGrad[n-1,j]は、現在のフレームの前回のフレームの第jのサブフレームと第(j+1)のサブフレームとの間のゲイン勾配であり、α_j+1≧α_jであり、

であり、j=0、1、2、・・・、I-2であり、開始サブフレームのサブフレームゲインは、
GainShapeTemp[n,0]=GainShape[n-1,I-1]+φ₁*GainGradFEC[0]、
GainShape[n,0]=GainShapeTemp[n,0]*φ₂を使用して取得され、
GainShape[n-1,I-1]は、第(n-1)のフレームの第(I-1)のサブフレームのサブフレームゲインであり、GainShape[n,0]は、現在のフレームの開始サブフレームのサブフレームゲインであり、GainShapeTemp[n,0]は、開始サブフレームのサブフレームゲインの中間値であり、0≦φ₁≦1.0であり、0<φ₂≦1.0であり、φ₁は、現在のフレームの前に受信した最終フレームのフレームクラスおよび第1のゲイン勾配の正または負の符号を使用して決定され、φ₂は、現在のフレームの前に受信した最終フレームのフレームクラスおよび現在のフレームより前の連続したロストフレームの数を使用して決定される。 According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes. In the case, the first gain slope is

GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, j] is the jth subframe and (j + 1) th of the previous frame of the current frame ) Gain gradient with respect to the subframe, α _{j + 1} ≧ α _j ,

J = 0, 1, 2,..., I-2, and the subframe gain of the start subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + φ ₁ * GainGradFEC [0],
GainShape [n, 0] = GainShapeTemp [n, 0] * φ 2 to be obtained using,
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the (n-1) th frame, and GainShape [n, 0] is the start of the current frame The subframe gain of the subframe, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 ≦ φ ₁ ≦ 1.0, 0 <φ ₂ ≦ 1.0, φ ₁ Is determined using the frame class of the last frame received before the current frame and the positive or negative sign of the first gain gradient, and φ ₂ is the frame of the last frame received before the current frame Determined using class and number of consecutive lost frames prior to current frame.

  According to this embodiment of the present invention, the determination module 720 uses the first gain gradient as a subframe before the last subframe of the previous frame of the current frame and the last subframe of the previous frame of the current frame. Subframe gain and first gain gradient of the last subframe of the previous frame of the current frame, and the frame class and current frame of the last frame received before the current frame Estimate the subframe gain of the starting subframe of the current frame according to the number of previous consecutive lost frames.

According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes. In the case, the first gain gradient is obtained using GainGradFEC [0] = GainGrad [n-1, I-2], where GainGradFEC [0] is the first gain gradient and GainGrad [n -1, I-2] is the gain gradient between the (I-2) th subframe and the (I-1) th subframe of the previous frame of the current frame, Frame gain is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + λ ₁ * GainGradFEC [0],
GainShapeTemp [n, 0] = min (λ ₂ * GainShape [n-1, I-1], GainShapeTemp [n, 0]),
Obtained using GainShape [n, 0] = max (λ ₃ * GainShape [n-1, I-1], GainShapeTemp [n, 0])
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the previous frame of the current frame, and GainShape [n, 0] is the subframe of the start subframe Gain, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 <λ ₁ <1.0, 1 <λ ₂ <2, 0 <λ ₃ <1.0 Yes, λ ₁ is determined using multiple relationships between the frame class of the last frame received before the current frame and the subframe gains of the last two subframes of the previous frame of the current frame, λ ₂ and λ ₃ are determined using the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame.

  According to this embodiment of the present invention, each frame includes I subframes, and the determination module 720 determines that the i-th subframe and (i + 1) th subframe of the previous frame of the current frame. And the gain gradient between the i-th subframe and (i + 1) -th subframe of the previous frame of the previous frame of the current frame, and the current gradient Estimate the gain gradient between the i-th subframe and the (i + 1) -th subframe of the frame, where i = 0, 1,..., I-2, and the current frame The weight occupied by the gain gradient between the i-th sub-frame and the (i + 1) -th sub-frame of the previous frame is the i-th sub-frame and the i-th sub-frame of the previous frame of the previous frame of the current frame. occupied by the gain gradient between (i + 1) subframes Greater than the weight, the determination module 720 determines the gain gradient between at least two subframes of the current frame and the subframe gain of the starting subframe of the current frame, and the frame class of the last frame received before the current frame and According to the number of consecutive lost frames before the current frame, subframe gains of other subframes excluding the start subframe of at least two subframes are estimated.

According to this embodiment of the invention, the gain gradient between at least two subframes of the current frame is
GainGradFEC [i + 1] = GainGrad [n-2, i] * β ₁ + GainGrad [n-1, i] * β ₂
GainGradFEC [i + 1] is the gain gradient between the i-th subframe and the (i + 1) -th subframe, and GainGrad [n-2, i] is the previous frame of the current frame. Gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, and GainGrad [n−1, i] is the i-th subframe of the previous frame of the current frame. Gain gradient between the frame and the (i + 1) th subframe, β ₂ > β ₁ , β ₂ + β ₁ = 1.0, i = 0, 1, 2,. I-2, and the subframe gain of other subframes excluding the start subframe of at least two subframes is
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] * β ₃ ,
Determined using GainShape [n, i] = GainShapeTemp [n, i] * β ₄
GainShape [n, i] is the subframe gain of the i-th subframe of the current frame, and GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame 0 ≦ β ₃ ≦ 1.0, 0 <β ₄ ≦ 1.0, and β ₃ is a plurality of relationships between GainGrad [n−1, i] and GainGrad [n−1, i + 1] and Determined using the positive or negative sign of GainGrad [n-1, i + 1], where β ₄ is the frame class of the last frame received before the current frame and consecutive lost before the current frame Determined using the number of frames.

  According to this embodiment of the invention, the determination module 720 performs weighted averaging on I gain gradients between (I + 1) subframes prior to the i-th subframe of the current frame. , Estimate the gain gradient between the i-th subframe and the (i + 1) -th subframe of the current frame, where i = 0, 1,..., I-2, The gain gradient between subframes close to i subframes occupies a greater weight, and the gain gradient between at least two subframes of the current frame and the subframe gain of the starting subframe of the current frame, and the current Other sub-frames excluding the start sub-frame of at least two sub-frames according to the frame class of the last frame received before the frame and the number of consecutive lost frames prior to the current frame Estimating the subframe gain frame.

According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes four subframes. The gain gradient between at least two subframes of the current frame is
GainGradFEC [1] = GainGrad [n-1,0] * γ ₁ + GainGrad [n-1,1] * γ ₂
+ GainGrad [n-1,2] * γ ₃ + GainGradFEC [0] * γ ₄ ,
GainGradFEC [2] = GainGrad [n-1,1] * γ ₁ + GainGrad [n-1,2] * γ ₂
+ GainGradFEC [0] * γ ₃ + GainGradFEC [1] * γ ₄ ,
GainGradFEC [3] = GainGrad [n-1,2] * γ ₁ + GainGradFEC [0] * γ ₂
Determined using + GainGradFEC [1] * γ ₃ + GainGradFEC [2] * γ ₄
GainGradFEC [j] is the gain gradient between the jth and (j + 1) th subframe of the current frame, and GainGrad [n-1, j] is the previous gradient of the current frame. Is the gain gradient between the jth subframe and the (j + 1) th subframe of the frame, j = 0, 1, 2,..., I-2, and γ ₁ + γ ₂ + γ ₃ + γ ₄ = 1.0, γ ₄ > γ ₃ > γ ₂ > γ ₁ and γ ₁ , γ ₂ , γ ₃ , and γ ₄ are determined using the frame class of the last frame received Subframe gains of other subframes excluding the start subframe of at least two subframes,
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i], where i = 1, 2, 3 and GainShapeTemp [n, 0] is the first gain gradient,
GainShapeTemp [n, i] = min (γ ₅ * GainShape [n-1, i], GainShapeTemp [n, i]),
GainShape [n, i] = max (γ ₆ * GainShape [n-1, i], GainShapeTemp [n, i])
GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame, i = 1, 2, and 3, and GainShape [n, i] is the 1st subframe gain of the current frame. subframe gain of i subframes, γ ₅ and γ ₆ are determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame, where 1 <γ ₅ < 2 and 0 ≦ γ ₆ ≦ 1.

  According to this embodiment of the invention, the determination module 720 determines the overall gain of the current frame according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. Estimate the gradient and estimate the overall gain of the current frame according to the overall gain gradient and the overall gain of the previous frame of the current frame of the current frame.

According to this embodiment of the present invention, the overall gain of the current frame is
Determined using GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the overall gain of the current frame, GainFrame_prevfrm is the overall gain of the previous frame of the current frame, and 0 <GainAtten ≦ 1.0 Yes, GainAtten is the overall gain gradient, and GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame.

  FIG. 8 is a schematic structural diagram of a decoding device 800 according to another embodiment of the present invention. The decoding device 800 includes a generation module 810, a determination module 820, and an adjustment module 830.

  If it is determined that the current frame is a lost frame, the generation module 810 synthesizes the high frequency band signal according to the decoding result of the previous frame of the current frame. The determination module 820 determines the subframe gain of at least two subframes of the current frame, and presents according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. The overall gain gradient of the current frame is estimated, and the overall gain of the current frame is estimated according to the overall gain gradient and the overall gain of the previous frame of the current frame. The adjustment module 830 obtains the high frequency band signal of the current frame, the high frequency band synthesized by the generation module according to the overall gain and subframe gain of at least two subframes determined by the determination module Adjust the signal.

  According to this embodiment of the invention, GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the overall gain of the current frame, GainFrame_prevfrm is the overall gain of the previous frame of the current frame, 0 <GainAtten ≦ 1.0, GainAtten is the overall gain gradient, and GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames before the current frame.

  FIG. 9 is a schematic structural diagram of a decoding device 900 according to an embodiment of the present invention. The decoding device 900 includes a processor 910, a memory 920, and a communication bus 930.

  In a situation where the processor 910 uses the communication bus 930 to determine that the current frame is a lost frame, the processor 910 synthesizes the high frequency band signal according to the decoding result of the previous frame of the current frame, and Determine the subframe gain of at least two subframes of the current frame according to the subframe gain of the subframe of at least one previous frame and the gain gradient between the subframes of at least one frame, and determine the overall current frame Stored in memory 920 to adjust the combined high frequency band signal according to the overall gain and the subframe gain of at least two subframes to determine the correct gain and obtain the high frequency band signal of the current frame Configured to launch code that has been

  According to this embodiment of the invention, processor 910 determines a subframe gain of a starting subframe of the current frame according to a subframe gain of a subframe of at least one frame and a gain gradient between the subframes of at least one frame. Determine the subframe gain of the other subframes excluding the start subframe of at least two subframes according to the subframe gain of the start subframe of the current frame and the gain gradient between the subframes of at least one frame decide.

  According to this embodiment of the present invention, processor 910 determines whether the last subframe of the previous frame of the current frame and the start subframe of the current frame according to the gain gradient between the subframes of the previous frame of the current frame. Estimate the first gain gradient between, and the subframe gain of the last subframe of the previous frame of the current frame and the subframe gain of the starting subframe of the current frame according to the first gain gradient, and at least 1 Estimate the gain gradient between at least two subframes of the current frame according to the gain gradient between the subframes of one frame, subtract the gain gradient between at least two subframes of the current frame and the start subframe of the current frame Of at least two subframes according to frame gain Estimating the sub-frame gains of other sub-frames excluding the start subframe.

  According to this embodiment of the invention, the processor 910 performs weighted averaging on the gain gradient between at least two subframes of the previous frame of the current frame to obtain a first gain gradient, and the current Current frame according to the subframe gain and first gain gradient of the last subframe of the previous frame, and the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame When the weighted averaging is performed, the gain gradient between subframes of the previous frame of the current frame closer to the current frame is weighted more heavily. Occupy.

を使用して取得され、GainGradFEC[0]は、第1のゲイン勾配であり、GainGrad[n-1,j]は、現在のフレームの前回のフレームの第jのサブフレームと第(j+1)のサブフレームとの間のゲイン勾配であり、α_j+1≧α_jであり、

であり、j=0、1、2、・・・、I-2であり、開始サブフレームのサブフレームゲインは、
GainShapeTemp[n,0]=GainShape[n-1,I-1]+φ₁*GainGradFEC[0]、
GainShape[n,0]=GainShapeTemp[n,0]*φ₂を使用して取得され、
GainShape[n-1,I-1]は、第(n-1)のフレームの第(I-1)のサブフレームのサブフレームゲインであり、GainShape[n,0]は、現在のフレームの開始サブフレームのサブフレームゲインであり、GainShapeTemp[n,0]は、開始サブフレームのサブフレームゲインの中間値であり、0≦φ₁≦1.0であり、0<φ₂≦1.0であり、φ₁は、現在のフレームの前に受信した最終フレームのフレームクラスおよび第1のゲイン勾配の正または負の符号を使用して決定され、φ₂は、現在のフレームの前に受信した最終フレームのフレームクラスおよび現在のフレームより前の連続したロストフレームの数を使用して決定される。 According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes. In the case, the first gain slope is

GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, j] is the jth subframe and (j + 1) th of the previous frame of the current frame ) Gain gradient with respect to the subframe, α _{j + 1} ≧ α _j ,

J = 0, 1, 2,..., I-2, and the subframe gain of the start subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + φ ₁ * GainGradFEC [0],
GainShape [n, 0] = GainShapeTemp [n, 0] * φ 2 to be obtained using,
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the (n-1) th frame, and GainShape [n, 0] is the start of the current frame The subframe gain of the subframe, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 ≦ φ ₁ ≦ 1.0, 0 <φ ₂ ≦ 1.0, φ ₁ Is determined using the frame class of the last frame received before the current frame and the positive or negative sign of the first gain gradient, and φ ₂ is the frame of the last frame received before the current frame Determined using class and number of consecutive lost frames prior to current frame.

  According to this embodiment of the present invention, the processor 910 uses, as the first gain gradient, a subframe before the last subframe of the previous frame of the current frame and a last subframe of the previous frame of the current frame, From the subframe gain and first gain gradient of the last subframe of the previous frame of the current frame, and the frame class and current frame of the last frame received before the current frame Estimate the subframe gain of the starting subframe of the current frame according to the number of previous consecutive lost frames.

According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes. In the case, the first gain gradient is obtained using GainGradFEC [0] = GainGrad [n-1, I-2], where GainGradFEC [0] is the first gain gradient and GainGrad [n -1, I-2] is the gain gradient between the (I-2) th subframe and the (I-1) th subframe of the previous frame of the current frame, Frame gain is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + λ ₁ * GainGradFEC [0],
GainShapeTemp [n, 0] = min (λ ₂ * GainShape [n-1, I-1], GainShapeTemp [n, 0]),
Obtained using GainShape [n, 0] = max (λ ₃ * GainShape [n-1, I-1], GainShapeTemp [n, 0])
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the previous frame of the current frame, and GainShape [n, 0] is the subframe of the start subframe Gain, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the starting subframe, 0 <λ ₁ <1.0, 1 <λ ₂ <2, 0 <λ ₃ <1.0 Yes, λ ₁ is determined using multiple relationships between the frame class of the last frame received before the current frame and the subframe gains of the last two subframes of the previous frame of the current frame, λ ₂ and λ ₃ are determined using the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame.

  According to this embodiment of the present invention, each frame includes I subframes, and the processor 910 includes the i-th subframe of the previous frame of the current frame and the (i + 1) th subframe. Weighted average between the current gradient and the gain gradient between the i-th and (i + 1) -th subframes of the previous frame of the previous frame of the current frame, and the current frame Estimate the gain gradient between the i-th subframe and the (i + 1) -th subframe, where i = 0, 1,..., I-2 and the previous time of the current frame The weight occupied by the gain gradient between the i-th subframe and the (i + 1) -th subframe of the current frame is the i-th subframe of the previous frame of the previous frame of the current frame and the ( The weight occupied by the gain gradient between i + 1) subframes Larger, the gain gradient between at least two subframes of the current frame and the subframe gain of the starting subframe of the current frame, and the frame class of the last frame received before the current frame and before the current frame According to the number of consecutive lost frames, subframe gains of other subframes excluding the start subframe of at least two subframes are estimated.

According to this embodiment of the invention, the gain gradient between at least two subframes of the current frame is
GainGradFEC [i + 1] = GainGrad [n-2, i] * β ₁ + GainGrad [n-1, i] * β ₂
GainGradFEC [i + 1] is the gain gradient between the i-th subframe and the (i + 1) -th subframe, and GainGrad [n-2, i] is the previous frame of the current frame. Gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, and GainGrad [n−1, i] is the i-th subframe of the previous frame of the current frame. Gain gradient between the frame and the (i + 1) th subframe, β ₂ > β ₁ , β ₂ + β ₁ = 1.0, i = 0, 1, 2,. I-2, and the subframe gain of other subframes excluding the start subframe of at least two subframes is
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] * β ₃ ,
Determined using GainShape [n, i] = GainShapeTemp [n, i] * β ₄
GainShape [n, i] is the subframe gain of the i-th subframe of the current frame, and GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame 0 ≦ β ₃ ≦ 1.0, 0 <β ₄ ≦ 1.0, and β ₃ is a plurality of relationships between GainGrad [n−1, i] and GainGrad [n−1, i + 1] and Determined using the positive or negative sign of GainGrad [n-1, i + 1], where β ₄ is the frame class of the last frame received before the current frame and consecutive lost before the current frame Determined using the number of frames.

  According to this embodiment of the invention, the processor 910 performs weighted averaging on I gain gradients between (I + 1) subframes prior to the i-th subframe of the current frame, Estimate the gain gradient between the i-th subframe and the (i + 1) -th subframe of the current frame, where i = 0, 1,..., I-2, and i The gain gradient between subframes close to the current subframe occupies a greater weight, and the gain gradient between at least two subframes of the current frame and the subframe gain of the start subframe of the current frame, and the current frame Other subframes excluding the start subframe of at least two subframes according to the frame class of the last frame received before the number of consecutive lost frames prior to the current frame Estimating the subframe gain beam.

According to this embodiment of the present invention, the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes four subframes. The gain gradient between at least two subframes of the current frame is
GainGradFEC [1] = GainGrad [n-1,0] * γ ₁ + GainGrad [n-1,1] * γ ₂
+ GainGrad [n-1,2] * γ ₃ + GainGradFEC [0] * γ ₄ ,
GainGradFEC [2] = GainGrad [n-1,1] * γ ₁ + GainGrad [n-1,2] * γ ₂
+ GainGradFEC [0] * γ ₃ + GainGradFEC [1] * γ ₄ ,
GainGradFEC [3] = GainGrad [n-1,2] * γ ₁ + GainGradFEC [0] * γ ₂
Determined using + GainGradFEC [1] * γ ₃ + GainGradFEC [2] * γ ₄
GainGradFEC [j] is the gain gradient between the jth and (j + 1) th subframe of the current frame, and GainGrad [n-1, j] is the previous gradient of the current frame. Is the gain gradient between the jth subframe and the (j + 1) th subframe of the frame, j = 0, 1, 2,..., I-2, and γ ₁ + γ ₂ + γ ₃ + γ ₄ = 1.0, γ ₄ > γ ₃ > γ ₂ > γ ₁ and γ ₁ , γ ₂ , γ ₃ , and γ ₄ are determined using the frame class of the last frame received Subframe gains of other subframes excluding the start subframe of at least two subframes,
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i], where i = 1, 2, 3 and GainShapeTemp [n, 0] is the first gain gradient,
GainShapeTemp [n, i] = min (γ ₅ * GainShape [n-1, i], GainShapeTemp [n, i]),
GainShape [n, i] = max (γ ₆ * GainShape [n-1, i], GainShapeTemp [n, i])
GainShapeTemp [n, i] is the intermediate value of the subframe gain of the i-th subframe of the current frame, i = 1, 2, and 3, and GainShape [n, i] is the 1st subframe gain of the current frame. subframe gain of i subframes, γ ₅ and γ ₆ are determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame, where 1 <γ ₅ < 2 and 0 ≦ γ ₆ ≦ 1.

  According to this embodiment of the invention, processor 910 determines the overall gain gradient of the current frame according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. And estimate the overall gain of the current frame according to the overall gain gradient and the overall gain of the previous frame of the current frame of the current frame.

  According to this embodiment of the invention, the overall gain of the current frame is determined using GainFrame = GainFrame_prevfrm * GainAtten, where GainFrame is the overall gain of the current frame, and GainFrame_prevfrm is the current gain. Is the overall gain of the previous frame, and 0 <GainAtten ≦ 1.0, GainAtten is the overall gain slope, GainAtten is the frame class of the last frame received and the current frame Determined using the number of consecutive lost frames.

  FIG. 10 is a schematic structural diagram of a decoding device 1000 according to an embodiment of the present invention. The decryption device 1000 includes a processor 1010, a memory 1020, and a communication bus 1030.

  In a situation where the processor 1010 uses the communication bus 1030 to determine that the current frame is a lost frame, the processor 1010 synthesizes the high frequency band signal according to the decoding result of the previous frame of the current frame, and Determines the subframe gain of at least two subframes of the current frame, and the overall gain gradient of the current frame according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame To estimate the overall gain of the current frame according to the overall gain gradient and the overall gain of the previous frame of the current frame, and to obtain the high frequency band signal of the current frame Combined high frequency according to the typical gain and subframe gain of at least two subframes The code stored in the memory 1020 is configured to activate to adjust the several band signal.

  According to this embodiment of the invention, GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the overall gain of the current frame, GainFrame_prevfrm is the overall gain of the previous frame of the current frame, 0 <GainAtten ≦ 1.0, GainAtten is the overall gain gradient, and GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames before the current frame.

  Those skilled in the art will appreciate that in the exemplary combinations described in the embodiments disclosed herein, the unit and algorithm steps may be implemented by electronic equipment or a combination of computer software and electronic equipment. Whether the function is performed by hardware or software depends on the specific application of the technical solution and design constraints. Different methods may be used by those skilled in the art to perform the functions described for each particular application, and such embodiments should not be considered as departing from the scope of the present invention.

  For the sake of simplicity and concise description, for detailed operational processes of the aforementioned systems, devices, and units, reference is made to the corresponding processes in the foregoing method embodiments, the details of which are described again herein. Those skilled in the art will clearly understand that this is not the case.

  It should be understood that in some embodiments provided in the present application, the disclosed systems, devices, and methods may be implemented in other ways. For example, the described apparatus embodiment is merely exemplary. For example, the unit division is merely logical function division and may be other division in the actual embodiment. For example, multiple units or components may be combined or integrated with another system, or some features may be ignored or not performed. Further, the illustrated or described interconnection or direct connection or communication connection may be implemented by using several interfaces. The indirect or communication connection between the device and the unit may be implemented electronically, mechanically, or in other forms.

  The unit described as a separate part may or may not be physically separate, and the part described as a unit may or may not be a physical unit, and is located in one place. It may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solution of the embodiments.

  Furthermore, the functional units in the embodiments of the present invention may be integrated into one processing unit, each of the units may physically exist alone, or two or more units may be combined into one unit. Integrated.

  If the functions are implemented in the form of software functional units and sold or used as independent products, the functions may be stored on a computer-readable storage medium. Based on such an understanding, basically, the technical solution of the present invention, or a part that contributes to the prior art, or some technical solutions may be implemented in the form of a software product. A computer software product is stored on a storage medium to instruct a computer device (which may be a personal computer, server, or network device) to perform all or part of the method steps described in the embodiments of the present invention. Including several instructions. The aforementioned storage medium can store program codes such as a USB flash drive, a removable hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Including any medium.

  The foregoing descriptions are merely specific embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present invention shall fall within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

700 Decryptor
710 generation module
720 decision module
730 Adjustment module
800 decoder
810 generation module
820 decision module
830 Adjustment module
910 processor
920 memory
930 communication bus
1010 processor
1020 memory
1030 Communication bus

Claims (36)

A decryption method,
In a situation where the current frame is determined to be a lost frame, synthesizing a high frequency band signal according to a decoding result of a previous frame of the current frame;
Determining a subframe gain of at least two subframes of the current frame according to a subframe gain of a subframe of at least one frame prior to the current frame and a gain gradient between the subframes of the at least one frame; Steps,
Determining a global gain for the current frame;
Adjusting the synthesized high frequency band signal according to the global gain and the subframe gain of the at least two subframes to obtain a high frequency band signal of the current frame. Determining a subframe gain of at least two subframes of the current frame according to a subframe gain of a subframe of at least one frame prior to the current frame and a gain gradient between the subframes of the at least one frame; The steps are
Determining a subframe gain of a starting subframe of the current frame according to the subframe gain of the subframe of the at least one frame and the gain gradient between the subframes of the at least one frame;
Other subframes excluding the start subframe of the at least two subframes according to the subframe gain of the start subframe of the current frame and the gain gradient between the subframes of the at least one frame And determining a subframe gain of the method. Determining a subframe gain of a starting subframe of the current frame according to the subframe gain of the subframe of the at least one frame and the gain gradient between the subframes of the at least one frame;
A first gain gradient between the last subframe of the previous frame of the current frame and the start subframe of the current frame according to a gain gradient between subframes of the previous frame of the current frame; Estimating, and
Estimating the subframe gain of the starting subframe of the current frame according to a subframe gain of the last subframe of the previous frame and a first gain gradient of the current frame. 2. The method according to 2. A first gain gradient between the last subframe of the previous frame of the current frame and the start subframe of the current frame according to a gain gradient between subframes of the previous frame of the current frame; The estimation step is
Performing weighted averaging on a gain gradient between at least two subframes of the previous frame of the current frame to obtain the first gain gradient, wherein the weighted averaging is performed. 4. The method of claim 3, comprising the step of gain gradients between subframes of the previous frame of the current frame close to the current frame occupying a greater weight. When the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes, the first frame The gain slope of is

Is obtained using
GainGradFEC [0] is the first gain gradient, and GainGrad [n−1, j] is the jth subframe and (j + 1) th subframe of the previous frame of the current frame. Gain gradient between and α _{j + 1} ≧ α _j

J = 0, 1, 2, ..., I-2,
The subframe gain of the starting subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + φ ₁ * GainGradFEC [0],
GainShape [n, 0] = GainShapeTemp [n, 0] * φ 2 to be obtained using,
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the (n-1) th frame, and GainShape [n, 0] is the current frame , GainShapeTemp [n, 0] is an intermediate value of subframe gains of the start subframe, 0 ≦ φ ₁ ≦ 1.0, and 0 <φ ₂ ≦ 1.0 Φ ₁ is determined using the frame class of the last frame received before the current frame and the positive or negative sign of the first gain gradient, and φ ₂ is the current frame The method according to claim 3 or 4, wherein the method is determined using the frame class of the last frame received before and the number of consecutive lost frames before the current frame. A first gain gradient between the last subframe of the previous frame of the current frame and the start subframe of the current frame according to a gain gradient between subframes of the previous frame of the current frame; The estimation step is
As the first gain gradient, a gain gradient between a subframe before the last subframe of the previous frame of the current frame and the last subframe of the previous frame of the current frame is used. 4. The method of claim 3, comprising the step of: When the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes, the first frame The gain slope of is obtained using GainGradFEC [0] = GainGrad [n-1, I-2]
GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, I-2] is the (I-2) th subframe and the (I2) of the previous frame of the current frame. -1) gain gradient between subframes,
The subframe gain of the starting subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + λ ₁ * GainGradFEC [0],
GainShapeTemp [n, 0] = min (λ ₂ * GainShape [n-1, I-1], GainShapeTemp [n, 0]),
Obtained using GainShape [n, 0] = max (λ ₃ * GainShape [n-1, I-1], GainShapeTemp [n, 0])
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the previous frame of the current frame, and GainShape [n, 0] is the start subframe The subframe gain of the frame, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the start subframe, 0 <λ ₁ <1.0, 1 <λ ₂ <2, 0 <λ ₃ <1.0, where λ ₁ is the number of frame classes of the last frame received before the current frame and the subframe gains of the last two subframes of the previous frame of the current frame Λ ₂ and λ ₃ are determined using the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. The person as claimed in claim 3 or 6 to be determined . Estimating the subframe gain of the starting subframe of the current frame according to a subframe gain of the last subframe of the previous frame of the current frame and the first gain gradient;
From the subframe gain and the first gain gradient of the last subframe of the previous frame of the current frame, and the frame class and the current frame of the last frame received before the current frame The method according to any one of claims 3 to 7, comprising estimating the subframe gain of the starting subframe of the current frame according to the number of previous consecutive lost frames. Other subframes excluding the start subframe of the at least two subframes according to the subframe gain of the start subframe of the current frame and the gain gradient between the subframes of the at least one frame The step of determining the subframe gain of
Estimating a gain gradient between the at least two subframes of the current frame according to the gain gradient between the subframes of the at least one frame;
The other of the at least two subframes excluding the start subframe according to the gain gradient between the at least two subframes of the current frame and the subframe gain of the start subframe of the current frame And estimating the subframe gain of the subframes. 9. The method according to any one of claims 2-8. Each frame includes I subframes, and estimating the gain gradient between the at least two subframes of the current frame according to the gain gradient between the subframes of the at least one frame comprises:
The gain gradient between the i-th subframe of the previous frame and the (i + 1) th subframe of the current frame and the i-th subframe of the previous frame of the previous frame of the current frame Performing a weighted averaging on the gain gradient between the frame and the (i + 1) th subframe, and between the i-th subframe and the (i + 1) th subframe of the current frame , I-2, and i-2, i-2, and the i-th subframe of the previous frame of the current frame and the (i + 1) -th The weight occupied by the gain gradient between the current frame and the current frame is the i-th subframe of the previous frame and the (i + 1) th subframe of the previous frame of the current frame. The weight occupied by the gain gradient between 10. The method of claim 9, further comprising: When the previous frame of the current frame is the (n-1) th frame and the current frame is the nth frame, between the at least two subframes of the current frame The gain slope of
GainGradFEC [i + 1] = GainGrad [n-2, i] * β ₁ + GainGrad [n-1, i] * β ₂
GainGradFEC [i + 1] is the gain gradient between the i-th subframe and the (i + 1) -th subframe, and GainGrad [n-2, i] is the previous gradient of the current frame. The gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, and GainGrad [n−1, i] is the current frame of the current frame The gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, β ₂ > β ₁ , β ₂ + β ₁ = 1.0, and i = 0, 1, 2, ... I-2,
The subframe gain of the other subframe excluding the start subframe of the at least two subframes is:
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] * β ₃ ,
Determined using GainShape [n, i] = GainShapeTemp [n, i] * β ₄
GainShape [n, i] is the subframe gain of the i-th subframe of the current frame, and GainShapeTemp [n, i] is the middle of the subframe gain of the i-th subframe of the current frame Value, 0 ≦ β ₃ ≦ 1.0, 0 <β ₄ ≦ 1.0, and β ₃ is a plurality between GainGrad [n−1, i] and GainGrad [n−1, i + 1]. And the positive or negative sign of GainGrad [n−1, i + 1], β ₄ is the frame class of the last frame received before the current frame and the current 11. A method according to claim 9 or 10, wherein the method is determined using the number of consecutive lost frames before a frame. Each frame includes I subframes, and estimating the gain gradient between the at least two subframes of the current frame according to the gain gradient between the subframes of the at least one frame comprises:
Performing weighted averaging on I gain gradients between (I + 1) subframes prior to the i-th subframe of the current frame; and the i-th subframe of the current frame; A step of estimating a gain gradient with respect to the (i + 1) th subframe, i = 0, 1,..., I-2, and between subframes close to the i th subframe 10. The method of claim 9, wherein the gain gradient of occupies a greater weight. When the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes four subframes, the current frame The gain gradient between the at least two subframes of a frame of
GainGradFEC [1] = GainGrad [n-1,0] * γ ₁ + GainGrad [n-1,1] * γ ₂
+ GainGrad [n-1,2] * γ ₃ + GainGradFEC [0] * γ ₄ ,
GainGradFEC [2] = GainGrad [n-1,1] * γ ₁ + GainGrad [n-1,2] * γ ₂
+ GainGradFEC [0] * γ ₃ + GainGradFEC [1] * γ ₄ ,
GainGradFEC [3] = GainGrad [n-1,2] * γ ₁ + GainGradFEC [0] * γ ₂
Determined using + GainGradFEC [1] * γ ₃ + GainGradFEC [2] * γ ₄
GainGradFEC [j] is the gain gradient between the jth subframe and the (j + 1) th subframe of the current frame, and GainGrad [n−1, j] is the current frame A gain gradient between the j-th subframe and the (j + 1) -th subframe of the previous frame, j = 0, 1, 2,..., I-2, and γ ₁ + γ ₂ + γ ₃ + γ ₄ = 1.0, γ ₄ > γ ₃ > γ ₂ > γ ₁ , and γ ₁ , γ ₂ , γ ₃ , and γ ₄ are the frame classes of the last frame received Is determined using
Subframe gains of the other subframes excluding the start subframe of the at least two subframes are:
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i], where i = 1, 2, 3 and GainShapeTemp [n, 0] is the first gain gradient ,
GainShapeTemp [n, i] = min (γ ₅ * GainShape [n-1, i], GainShapeTemp [n, i]),
GainShape [n, i] = max (γ ₆ * GainShape [n-1, i], GainShapeTemp [n, i])
i = 1, 2, and 3, GainShapeTemp [n, i] is an intermediate value of the subframe gain of the i-th subframe of the current frame, and GainShape [n, i] is the current Subframe gain of the i th subframe of the frame, γ ₅ and γ _{6 using the} frame class of the last frame received and the number of consecutive lost frames prior to the current frame 13. The method according to claim 9 or 12, wherein the method is determined, 1 <γ ₅ <2, and 0 ≦ γ ₆ ≦ 1. The other of the at least two subframes excluding the start subframe according to the gain gradient between the at least two subframes of the current frame and the subframe gain of the start subframe of the current frame Estimating the subframe gain of subframes of
The gain gradient between the at least two subframes of the current frame and the subframe gain of the starting subframe of the current frame; and the frame class of the last frame received before the current frame and Estimating the subframe gain of the other subframe excluding the starting subframe of the at least two subframes according to the number of consecutive lost frames prior to the current frame. The method according to any one of 9 to 13. Estimating the global gain of the current frame comprises:
Estimating a global gain gradient of the current frame according to the frame class of the last frame received before the current frame and the number of consecutive lost frames prior to the current frame;
15. Estimating the global gain of the current frame according to the global gain gradient and the global gain of the previous frame of the current frame. The global gain of the current frame is
GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the global gain of the current frame, GainFrame_prevfrm is the global gain of the previous frame of the current frame, and 0 <GainAtten ≦ 1.0 GainAtten is the global gain gradient, and GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame. 15. The method according to 15. A decryption method,
In a situation where the current frame is determined to be a lost frame, synthesizing a high frequency band signal according to a decoding result of a previous frame of the current frame;
Determining a subframe gain of at least two subframes of the current frame;
Estimating a global gain gradient of the current frame according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame;
Estimating the global gain of the current frame according to the global gain gradient and the global gain of the previous frame of the current frame;
Adjusting the synthesized high frequency band signal according to the global gain and the subframe gain of the at least two subframes to obtain a high frequency band signal of the current frame. The global gain of the current frame is
GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the global gain of the current frame, GainFrame_prevfrm is the global gain of the previous frame of the current frame, and 0 <GainAtten ≦ 1.0 GainAtten is the global gain gradient, and GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame. The method according to 17. In a situation where a current frame is determined to be a lost frame, a generation module configured to synthesize a high frequency band signal according to a decoding result of a previous frame of the current frame;
Determining a subframe gain of at least two subframes of the current frame according to a subframe gain of a subframe of at least one frame prior to the current frame and a gain gradient between the subframes of the at least one frame; A determination module configured to determine a global gain of the current frame;
The high frequency band synthesized by the generation module according to the global gain and the subframe gain of the at least two subframes determined by the determination module to obtain a high frequency band signal of the current frame A decoding device comprising: an adjustment module configured to adjust the signal.   The determination module determines a subframe gain of a start subframe of the current frame according to the subframe gain of the subframe of the at least one frame and the gain gradient between the subframes of the at least one frame. The other sub-frames of the at least two sub-frames excluding the start sub-frame according to the sub-frame gain of the start sub-frame of the current frame and the gain gradient between the sub-frames of the at least one frame 20. The decoding device according to claim 19, wherein the decoding device determines a subframe gain of a frame.   The determination module is configured to determine a first subframe between the last subframe of the previous frame and the start subframe of the current frame according to a gain gradient between subframes of the previous frame of the current frame. Estimating a gain gradient of 1 and estimating the subframe gain of the starting subframe of the current frame according to the subframe gain of the last subframe of the previous frame of the current frame and the first gain gradient The decoding device according to claim 20, wherein   The determination module performs weighted averaging on a gain gradient between at least two subframes of the previous frame of the current frame, obtains the first gain gradient, and performs the weighted averaging. 22. The decoding device according to claim 21, wherein a gain gradient between subframes of the previous frame of the current frame close to the current frame occupies a greater weight. When the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes, the first frame The gain slope of is

Is obtained using
GainGradFEC [0] is the first gain gradient, and GainGrad [n−1, j] is the jth subframe and (j + 1) th subframe of the previous frame of the current frame. Gain gradient between and α _{j + 1} ≧ α _j

J = 0, 1, 2, ..., I-2,
The subframe gain of the starting subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + φ ₁ * GainGradFEC [0],
GainShape [n, 0] = GainShapeTemp [n, 0] * φ 2 to be obtained using,
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the (n-1) th frame, and GainShape [n, 0] is the current frame , GainShapeTemp [n, 0] is an intermediate value of subframe gains of the start subframe, 0 ≦ φ ₁ ≦ 1.0, and 0 <φ ₂ ≦ 1.0 Φ ₁ is determined using the frame class of the last frame received before the current frame and the positive or negative sign of the first gain gradient, and φ ₂ is the current frame 23. The decoding device according to claim 21 or 22, wherein the decoding device is determined using the frame class of the last frame received before and the number of consecutive lost frames before the current frame.   The determination module may determine, as the first gain gradient, between a subframe prior to the last subframe of the previous frame of the current frame and the final subframe of the previous frame of the current frame. 23. The decoding device according to claim 21, wherein a gain gradient is used. When the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes I subframes, the first frame The gain slope of is obtained using GainGradFEC [0] = GainGrad [n-1, I-2]
GainGradFEC [0] is the first gain gradient, and GainGrad [n-1, I-2] is the (I-2) th subframe and the (I2) of the previous frame of the current frame. -1) gain gradient between subframes,
The subframe gain of the starting subframe is
GainShapeTemp [n, 0] = GainShape [n-1, I-1] + λ ₁ * GainGradFEC [0],
GainShapeTemp [n, 0] = min (λ ₂ * GainShape [n-1, I-1], GainShapeTemp [n, 0]),
Obtained using GainShape [n, 0] = max (λ ₃ * GainShape [n-1, I-1], GainShapeTemp [n, 0])
GainShape [n-1, I-1] is the subframe gain of the (I-1) th subframe of the previous frame of the current frame, and GainShape [n, 0] is the start subframe The subframe gain of the frame, GainShapeTemp [n, 0] is the intermediate value of the subframe gain of the start subframe, 0 <λ ₁ <1.0, 1 <λ ₂ <2, 0 <λ ₃ <1.0, where λ ₁ is the number of frame classes of the last frame received before the current frame and the subframe gains of the last two subframes of the previous frame of the current frame Λ ₂ and λ ₃ are determined using the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. The recovery of claim 21 or 24, as determined. Device.   The determination module includes the subframe gain and the first gain gradient of the last subframe of the previous frame of the current frame, and the frame class of the last frame received before the current frame and The decoding device according to any one of claims 21 to 25, wherein the subframe gain of the start subframe of the current frame is estimated according to the number of the consecutive lost frames before the current frame.   The determination module estimates a gain gradient between the at least two subframes of the current frame according to the gain gradient between the subframes of the at least one frame, and the at least two subframes of the current frame Estimating the subframe gain of the other subframes excluding the start subframe of the at least two subframes according to the gain gradient in between and the subframe gain of the start subframe of the current frame 27. The decoding device according to any one of claims 20 to 26.   Each frame includes I subframes, and the determination module includes a gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame of the current frame, and the Performing a weighted average on the gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame of the previous frame of the current frame, and the i-th of the current frame Estimating the gain gradient between the subframe and the (i + 1) th subframe, i = 0, 1, ..., I-2, and the first frame of the previous frame of the current frame The weight occupied by the gain gradient between the i subframe and the (i + 1) th subframe is equal to the i th subframe of the previous frame of the previous frame of the current frame. Between the (i + 1) th subframe Weights greater than occupied by the serial gain slope, the decoding apparatus according to claim 27. The gain gradient between the at least two subframes of the current frame is
GainGradFEC [i + 1] = GainGrad [n-2, i] * β ₁ + GainGrad [n-1, i] * β ₂
GainGradFEC [i + 1] is the gain gradient between the i-th subframe and the (i + 1) -th subframe, and GainGrad [n-2, i] is the previous gradient of the current frame. The gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, and GainGrad [n−1, i] is the current frame of the current frame The gain gradient between the i-th subframe and the (i + 1) -th subframe of the previous frame, β ₂ > β ₁ , β ₂ + β ₁ = 1.0, and i = 0, 1, 2, ... I-2,
The subframe gain of the other subframe excluding the start subframe of the at least two subframes is:
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i] * β ₃ ,
Determined using GainShape [n, i] = GainShapeTemp [n, i] * β ₄
GainShape [n, i] is the subframe gain of the i-th subframe of the current frame, and GainShapeTemp [n, i] is the middle of the subframe gain of the i-th subframe of the current frame Value, 0 ≦ β ₃ ≦ 1.0, 0 <β ₄ ≦ 1.0, and β ₃ is a plurality between GainGrad [n−1, i] and GainGrad [n−1, i + 1]. And the positive or negative sign of GainGrad [n−1, i + 1], β ₄ is the frame class of the last frame received before the current frame and the current 29. Decoding device according to claim 27 or 28, determined using the number of consecutive lost frames before a frame.   The determination module performs weighted averaging on I gain gradients between (I + 1) subframes prior to the i-th subframe of the current frame, and determines the i-th subframe of the current frame. Estimating the gain gradient between the subframe and the (i + 1) th subframe, i = 0, 1,..., I-2, and between subframes close to the i th subframe 28. The decoding device according to claim 27, wherein the gain gradient occupies a greater weight. When the previous frame of the current frame is the (n-1) th frame, the current frame is the nth frame, and each frame includes four subframes, the current frame The gain gradient between the at least two subframes of a frame of
GainGradFEC [1] = GainGrad [n-1,0] * γ ₁ + GainGrad [n-1,1] * γ ₂
+ GainGrad [n-1,2] * γ ₃ + GainGradFEC [0] * γ ₄ ,
GainGradFEC [2] = GainGrad [n-1,1] * γ ₁ + GainGrad [n-1,2] * γ ₂
+ GainGradFEC [0] * γ ₃ + GainGradFEC [1] * γ ₄ ,
GainGradFEC [3] = GainGrad [n-1,2] * γ ₁ + GainGradFEC [0] * γ ₂
Determined using + GainGradFEC [1] * γ ₃ + GainGradFEC [2] * γ ₄
GainGradFEC [j] is the gain gradient between the jth subframe and the (j + 1) th subframe of the current frame, and GainGrad [n−1, j] is the current frame A gain gradient between the j-th subframe and the (j + 1) -th subframe of the previous frame, j = 0, 1, 2,..., I-2, and γ ₁ + γ ₂ + γ ₃ + γ ₄ = 1.0, γ ₄ > γ ₃ > γ ₂ > γ ₁ , and γ ₁ , γ ₂ , γ ₃ , and γ ₄ are the frame classes of the last frame received Is determined using
The subframe gain of the other subframe excluding the start subframe of the at least two subframes is:
GainShapeTemp [n, i] = GainShapeTemp [n, i-1] + GainGradFEC [i], where i = 1, 2, 3 and GainShapeTemp [n, 0] is the first gain gradient ,
GainShapeTemp [n, i] = min (γ ₅ * GainShape [n-1, i], GainShapeTemp [n, i]),
GainShape [n, i] = max (γ ₆ * GainShape [n-1, i], GainShapeTemp [n, i])
GainShapeTemp [n, i] is an intermediate value of subframe gains of the i-th subframe of the current frame, i = 1, 2, and 3, and GainShape [n, i] is the current Subframe gain of the i th subframe of the frame, γ ₅ and γ _{6 using the} frame class of the last frame received and the number of consecutive lost frames prior to the current frame 31. The decoding device according to claim 27 or 30, wherein the decoding device is determined such that 1 <γ ₅ <2 and 0 ≦ γ ₆ ≦ 1.   The determination module includes the gain gradient between the at least two subframes of the current frame and the subframe gain of the start subframe of the current frame, and the final frame received before the current frame. Estimating the subframe gains of the other subframes excluding the starting subframe of the at least two subframes according to the frame class of and the number of consecutive lost frames prior to the current frame; 32. The decoding device according to any one of claims 27 to 31. The determination module estimates a global gain gradient of the current frame according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame;
33. The decoding device according to claim 19, wherein the global gain of the current frame is estimated according to the global gain gradient and the global gain of the previous frame of the current frame of the current frame. . The global gain of the current frame is
GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the global gain of the current frame, GainFrame_prevfrm is the global gain of the previous frame of the current frame, and 0 <GainAtten ≦ 1.0 GainAtten is the global gain gradient, and GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame. 34. The decoding device according to 33. In a situation where a current frame is determined to be a lost frame, a generation module configured to synthesize a high frequency band signal according to a decoding result of a previous frame of the current frame;
Determining a subframe gain of at least two subframes of the current frame, and determining the current frame according to the frame class of the last frame received before the current frame and the number of consecutive lost frames before the current frame. A determination module configured to estimate a global gain gradient of a frame and to estimate a global gain of the current frame according to the global gain gradient and the global gain of the previous frame of the current frame;
The high frequency band synthesized by the generation module according to the global gain and the subframe gain of the at least two subframes determined by the determination module to obtain a high frequency band signal of the current frame A decoding device comprising: an adjustment module configured to adjust the signal.   GainFrame = GainFrame_prevfrm * GainAtten, GainFrame is the global gain of the current frame, GainFrame_prevfrm is the global gain of the previous frame of the current frame, and 0 <GainAtten ≦ 1.0, GainAtten 36. The global gain slope of claim 35, wherein GainAtten is determined using the frame class of the last frame received and the number of consecutive lost frames prior to the current frame. Decoding device.

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